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The American obstetric system has, for most of its modern history, been organized around a delivery event. Nine months of prenatal care, the delivery, and then a single postpartum visit at six weeks. If a woman was covered by pregnancy-related Medicaid, her coverage extended to 60 days after the end of her pregnancy and then terminated. Whether she had recovered fully, whether she had developed new symptoms, whether her blood pressure had normalized, whether her mental health was stable, whether she had a primary care physician: these were questions whose answers were not required before the coverage clock ran out.

This structure did not emerge from clinical evidence about postpartum recovery timelines. It emerged from a legislative decision in which 60 days was deemed sufficient. The clinical evidence has been accumulating for decades to suggest that 60 days is far from sufficient, and that the deaths occurring after that coverage cliff are not random. They fall disproportionately on Black women, on low-income women, on the women whose Medicaid coverage ends precisely when their clinical vulnerability continues.

The fourth trimester, the 12-week period following delivery during which the maternal body undergoes dramatic physiological change, has received increasing clinical attention over the past decade. It has not received proportional policy attention. And the mismatch between the clinical reality of postpartum risk and the policy structure of postpartum coverage is one of the most concrete and tractable sources of excess maternal mortality in the United States.

ObGyn Intelligence: Safety analysis, the evidence critique, and the verdict are below -- for subscribers who want the full picture.

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“Reproductive freedom” has become one of the most repeated phrases in American medicine.

The major medical groups use it constantly.

When they do say ‘reproductive freedom’, they almost always mean one thing: abortion.

ACOG says abortion is essential health care and that it is committed to protecting and increasing access to it.

The American Medical Association says much the same.

The phrase appears on banners, in press releases, and in the title of a federal bill. Strip away the packaging and the message is direct: a woman should be able to end a pregnancy without the government standing in her way.

I do not write to argue about abortion. I write to argue about the word “freedom,” because the people using it have made it far too small.

I say that reproductive freedom means that every woman independent of her race, religion, or insurance status should be allowed to see the doctor of her choice.

Freedom to make a choice means nothing if you cannot reach a doctor to carry it out.

A right you cannot use is not a right. It is a slogan.

And for millions of American women, the door to ordinary obstetric and gynecologic care is harder to open than the speeches admit.

Start with who pays. Medicaid covers about 4 in 10 births in the United States. For Black mothers the share is far higher, around 64 to 65 percent. For Hispanic mothers it is close to 59 percent. For white mothers it is about 28 percent.

Roughly 1 in 4 Black women of any age relies on Medicaid for her health coverage. If you want to talk about reproductive freedom for Black women, you are, whether you say so or not, talking about Medicaid.

Here is the part the slogans skip. Having Medicaid is not the same as having a doctor. A doctor must agree to see you. And neither ACOG nor the AMA says that doctors have to. Or that they should. They are mum about it. Silence.

The AMA Code of Medical Ethics is explicit. A physician is, except in an emergency, free to choose whom to serve. The Code says doctors are not ethically required to accept every patient who asks. The duty to treat without regard to payment is framed as a shared, voluntary, professional ideal, not a rule any individual doctor must follow. ACOG, for its part, publishes statement after statement demanding that lawmakers, hospitals, and insurers expand access. I have read them closely. They ask everyone else to act. They do not tell their own members that each of them must take Medicaid patients.

That silence is the whole game.

So what happens in the real world? On paper, ob-gyns accept Medicaid at higher rates than most specialties. But “accepts Medicaid” is a stated policy, not a delivered service, and the gap between the two is where access quietly dies. A practice can say yes and still cap how many Medicaid patients it takes each month, close its Medicaid panel while keeping the private one open, push those patients into a single crowded clinic session, or simply offer them a later date. National survey data show that even among doctors with open appointments, a meaningful share decline Medicaid patients outright. One widely cited survey found about a quarter of primary care practices were not accepting new Medicaid patients at all. A separate physician survey in large cities found only about 55 percent of ob-gyns accepting Medicaid, and that number measures willingness to bill the program, not a guaranteed appointment.

Researchers have tested this directly by posing as patients and calling offices. The picture is uneven, and I will report it honestly. For routine general ob-gyn visits, a 2024 nationwide study found Medicaid callers waited only about 5 percent longer than privately insured callers, a difference that was not statistically significant. The bigger barrier there is not the wait. It is whether the office takes the insurance at all. But for subspecialty care, the gap is stark. A 2023 national study of ob-gyn subspecialists found Medicaid patients waited 44 percent longer for a new appointment, and the difference was highly significant. When a woman needs a gynecologic oncologist for a cancer, a maternal-fetal medicine specialist for a high-risk pregnancy, or a urogynecologist for a prolapse, her insurance card changes how long she waits and sometimes whether she is seen at all. No law requires any private insurer or Medicaid plan to keep a gynecologic oncologist in network.

The standard answer is that these women can go to a community health center or a hospital clinic. Many can, and the people who work in those clinics are often excellent. But notice the language we have settled on. We call them “safety-net” clinics. The word quietly tells the patient that this is the place you land when you fall, the catch beneath the trapeze, the option of last resort. It frames public-insurance care as a step below the care everyone else gets, a separate and lesser track. I think the term itself is a misnomer that does real harm. A clinic that delivers good medicine is not a net under a fall. It is medicine. When we label it a safety net, we have already conceded that Medicaid patients are routed to a different door, and we have made that routing sound like generosity instead of segregation by payment.

This is the contradiction at the center of the reproductive freedom campaign. The same organizations that say abortion access is a right do not say that ordinary obstetric and gynecologic care is one their members must provide regardless of insurance. They will fight a state legislature over a gestational limit. They will not tell a private practice in a wealthy suburb that it has to open its schedule to the Medicaid patient across town. The first fight is public and political. The second would cost their members money. Guess which one stays unspoken.

What it means for patients is simple and hard. If you carry Medicaid, your “freedom” depends on finding a physician who will actually see you, in a timely way, for the full range of care you might need, from contraception to a complicated delivery to a cancer. That freedom is real for some women and theoretical for others, and which group you land in has a great deal to do with your zip code, your insurer, and your race. A right that strong for the privately insured and thin for the publicly insured is not equal freedom. It is freedom rationed by payment.

My take is direct. Reproductive freedom that begins and ends at abortion is too narrow to deserve the name. Real reproductive freedom is the ability of any woman to obtain the full range of reproductive health care, including the care that keeps her alive during pregnancy and the care that catches her cancer early, no matter how she is insured. Until ACOG and the AMA are willing to say plainly that accepting Medicaid is a professional obligation and not an act of charity, their support for patients is partial. They are defending the choice while leaving the door to the exam room half closed. I have spent fifty years inside this system. I have watched the speeches get bolder and the schedules stay the same. The women covered by Medicaid, who are disproportionately Black, are owed more than a slogan. They are owed an appointment.

If a professional society means what it says about reproductive freedom, the test is not how loudly it opposes a law. The test is whether it will ask its own members to take the patient who pays the least. Watch for who says it out loud.

Share this with a colleague who uses the phrase “reproductive freedom” without asking who actually has it. The data should travel. ObGyn Intelligence is free; a paid subscription keeps it independent.

References

1. American College of Obstetricians and Gynecologists. Abortion policy. Statement of policy. Washington, DC: ACOG; 2025.

2. American Medical Association. Abortion: preserving access to reproductive health services. D-5.999. Chicago, IL: AMA; 2025.

3. American Medical Association. Prospective patients. Code of Medical Ethics Opinion 1.1.2. Chicago, IL: AMA. Accessed June 23, 2026.

4. American Medical Association. Physician exercise of conscience. Code of Medical Ethics Opinion 1.1.7. Chicago, IL: AMA. Accessed June 23, 2026.

5. Osterman MJK, Martin JA. Primary source of payment for the delivery: United States, 2021. NCHS Data Brief No. 468. Hyattsville, MD: National Center for Health Statistics; 2023.

6. March of Dimes. Medicaid coverage by race/ethnicity: United States, 2021-2023 average. PeriStats. Accessed June 23, 2026.

7. Medicaid and CHIP Payment and Access Commission. Physician acceptance of new Medicaid patients: findings from the National Electronic Health Records Survey. Washington, DC: MACPAC; 2021.

8. Holgash K, Heberlein M. Physician acceptance of new Medicaid patients: what matters and what doesn’t. Health Affairs Forefront. April 10, 2019.

9. Kyllo HM, Bresnitz W, Bickner M, Matous MA, Mulenga NM, O’Brien EA, et al. Access to general obstetrics and gynecology care among Medicaid beneficiaries and the privately insured: a nationwide mystery caller study in the USA. Minerva Obstet Gynecol. 2024;76(5):444-451.

10. Corbisiero MF, Tolbert B, Sanches M, Shelden N, Hachicha Y, Dao H, et al. Medicaid coverage and access to obstetrics and gynecology subspecialists: findings from a national mystery caller study in the United States. Am J Obstet Gynecol. 2023;228(6):722.e1-722.e9.

11. Knisely AT, Michael JE, Eskander RN, et al. Insurance-mediated disparities in gynecologic oncology care. Obstet Gynecol. 2022;139(2):305-312.

Menopause is the clearest split in the series: tested drugs that target the brain circuit behind hot flashes, versus an untested peptide market.

Menopause has its own peptide story, and it splits cleanly in two. On one side, two new nonhormonal drugs that quiet hot flashes by targeting the brain circuit that produces them. On the other, a peptide clinic market selling injections for menopause with almost no evidence behind them. Both use the word peptide. Only one side has trials.

Hot flashes start in the brain. A cluster of neurons that helps run the body’s thermostat uses signals including a peptide called neurokinin B. As estrogen falls in menopause, those neurons fire too easily, and the result is the sudden heat and sweat that millions of women know. The new drugs do not replace a peptide. They block the receptor that the overactive signal lands on. That is pathway pharmacology, and it went through clinical trials.

A woman who cannot or does not want to take estrogen now has real nonhormonal options that work. She also faces a market that will sell her unrelated injections labeled peptide therapy for the same symptoms. The difference between the two is the difference between a tested drug and a hopeful guess, and it is worth real money and real safety.

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About one in three American adults now ask an AI chatbot a health question in a given year.

That is roughly the same share that turns to social media for health information. So a quiet research question has become an urgent one: when should a medical AI keep its mouth shut?

A new review in npj Digital Medicine pulls together the young science of “abstention” — a technical word for a simple idea.

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It means knowing when to stop, ask for more information, or just say “I don’t know.” The finding is blunt. Today’s models almost never do it. In one study, a leading model held back on only 1 in 10 medical questions that had no safe answer. The rest of the time it answered anyway, fluent, confident, and often wrong.

So researchers are building tools to teach AI when to stay quiet. And the behavior they score as “safe,” again and again, is the same: when in doubt, the model should stop and say, “Please consult a doctor.”

That sounds right.

It is also where the thinking stops.

The whole approach rests on one assumption nobody checks: that the doctor you are sent to will do better.

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Start with the machine. When an AI gives a confident answer it has no real basis for, we now call it confabulation — making up a plausible story to fill a gap in what it actually knows. The field takes this seriously. There are now careful tools that catch a model in the act of inventing, by sampling its answers and flagging when its confidence is hollow. Good work, and necessary.

But confabulation is not a computer glitch. It is what any mind does, silicon or human, when it is unsure and feels pressure to produce an answer.

Daniel Kahneman spent a career proving this about people. He showed that confidence is not a readout of accuracy. It is a feeling, produced by how good a story the mind can tell itself. He called it the illusion of validity: we feel sure when the pieces fit, even when the evidence is thin. And he named the engine, the mind builds its answer from whatever is in front of it and quietly ignores what is missing.

Read that again. It describes a language model. It also describes a tired clinician at 3 a.m.

The evidence on doctors is not gentle. In a landmark 2008 review, two patient-safety researchers argued that physicians badly underrate how often their diagnoses are wrong. When one of them asked rooms full of doctors whether they had made a diagnostic error in the past year, only about 1 in 100 said yes. A 2013 study put numbers on the gap. Physicians got about 55 out of 100 easy cases right — and fewer than 6 out of 100 hard ones. Their confidence barely moved: about 7.2 out of 10 on the easy cases, 6.4 on the hard ones. Accuracy fell off a cliff. Confidence took a gentle stroll downhill. On the hard cases they were wrong more than nine times in ten and stayed nearly as sure of themselves.

What they almost never said was “I don’t know.” That phrase is in remarkably few doctors’ repertoires. The culture of medicine treats uncertainty as something close to failure, and tolerance for not-knowing measurably drops over the course of medical school. We train it out of people.

So here is the asymmetry the AI field keeps walking past. We have built detectors to catch the machine bluffing. We have built nothing of the kind for the clinician who does the same thing, undetected, and then writes it in the chart. The machine that confabulates gets a benchmark and a leaderboard. The human it defers to gets the benefit of the doubt.

In obstetrics this matters twice over, because there are two patients. And there is a second blind spot in the research: it treats silence as the safe choice. At the bedside it often is not. The harm in my field comes far more from things not done, the warning sign not named, the worrying tracing not acted on, the escalation that came too late, than from saying too much. An AI trained to go quiet whenever it is unsure can be trained straight into the passivity that hurts patients. And “consult a doctor” assumes there is a doctor to consult, and that the doctor knows. For many people typing questions at midnight, neither is true.

This is the part the silence debate misses, and it is the heart of a clinical opinion my colleagues and I just published. The danger was never that clinicians would use AI. The danger is that they will use it poorly, copying confident output without checking it, accepting an error because it reads well, treating a chatbot as an authority instead of a tool. The answer is not to avoid these tools, and it is not to make them quieter. It is competence: knowing when AI helps and when it does not, how to test its answers, how to catch a made-up drug dose before it reaches a patient, how to say plainly what is known and what is not, and who stays accountable when the screen goes dark. The clinician does.

Obstetrics has done this before. Ultrasound, electronic fetal monitoring, cell-free DNA screening, each arrived to resistance, and none of them replaced the obstetrician’s judgment. Each demanded a higher version of it. AI is next in that line.

But notice what that competence really asks for. Knowing the limits of your own knowledge. Saying “I don’t know” out loud. Matching your confidence to what you actually checked. That is exactly the calibration we are now trying to engineer into the machine — and it is the same skill medicine has never seriously required of itself.

For a patient, the practical version is small and powerful. The AI article you read before your appointment can sound certain and be wrong. So can the confident answer across the desk. The clinician worth trusting is not the one who never hesitates.

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It is the one who can say, “I’m not sure: let me check,” and then actually checks.

You are allowed to ask two questions out loud: How sure are you? And did you verify it? A good doctor will not flinch at either.

My take: we are studying the wrong half of the problem. The question was never silence versus speech. It is calibration — knowing what you know, and admitting what you don’t. Kahneman built his entire method on the premise that he could not trust his own confidence, so he backed everything with data. That is the discipline. It is rare in the model. It is just as rare in the physician we hold up as the safe alternative.

The arrival of AI is forcing a calibration test on medicine that medicine never volunteered for. We should take it. The goal is not a quieter machine. It is a profession — human and artificial together — that is finally honest about the edge of its own knowledge. In obstetrics, where two patients depend on that honesty, “first do no change” has to give way to “first do good.” Competence, not silence, is the safer answer.

Bottom line: don’t ask how to make AI stay quiet. Ask how to make both the machine and the clinician tell you the truth about how much they actually know.

If this is the kind of thinking you want more of, subscribe to ObGyn Intelligence. It is free, it is independent, and it says plainly what the data shows.

References

1. Presacan O, Nik A, Ojha J, Thambawita V, Ionescu B, Riegler MA. When silence is safer: a review and decision-theoretic framework for LLM abstention in healthcare. npj Digit Med. 2026. doi:10.1038/s41746-026-02882-1.

2. Grünebaum A, Dudenhausen J, Chervenak FA. Clinical artificial intelligence competence in obstetrics and gynecology: patient safety, physician accountability, and responsible use. Am J Obstet Gynecol. 2026. doi:10.1016/j.ajog.2026.06.011.

3. Berner ES, Graber ML. Overconfidence as a cause of diagnostic error in medicine. Am J Med. 2008;121(5 Suppl):S2-S23. doi:10.1016/j.amjmed.2008.01.001.

4. Meyer AND, Payne VL, Meeks DW, Rao R, Singh H. Physicians’ diagnostic accuracy, confidence, and resource requests: a vignette study. JAMA Intern Med. 2013;173(21):1952-1958. doi:10.1001/jamainternmed.2013.10081.

5. Kahneman D. Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux; 2011.

6. Grünebaum A, Chervenak J, Pollet SL, Katz A, Chervenak FA. The exciting potential for ChatGPT in obstetrics and gynecology. Am J Obstet Gynecol. 2023;228:696-705. doi:10.1016/j.ajog.2023.03.009.

The most urgent peptide issue in obstetrics now, reduced to three numbers every woman on a GLP-1 should know.

Millions of women of reproductive age now take a GLP-1 drug. That makes semaglutide and tirzepatide preconception drugs, whether obstetrics planned for it or not. And the real risks around pregnancy are not the ones that make people panic.

GLP-1 drugs are not recommended during pregnancy. Animal studies suggest possible harm, and human data are still limited. So the standard advice is to stop before trying to conceive. The drug labels build in a washout window because these medications stay in the body for weeks. The catch is that fertility can return as weight drops, sometimes before a woman expects it.

Three numbers decide how this goes, and most women never hear them: how long before conception to stop the drug, whether the drug weakens the pill, and what happens to the pregnancy if the drug is stopped abruptly at the wrong moment. Get those three right and the picture is reassuring. Miss them and the avoidable problems show up.

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Reddit is where patients and others go at 2 a.m. when they are scared and do not want to bother anyone. Finding the right posts is not simple -- I monitor dozens of communities, filtering thousands of threads for those that reveal something clinically meaningful: a dangerous myth gaining traction, a gap between patient belief and evidence, or a story that guidelines cannot capture. This series -- ObGyn Intelligence on Reddit -- dissects those posts against the medical literature, because ObGyns who ignore social media are ignoring the most unfiltered window into what our patients think, fear, and do between appointments.

Summary

A first-time mother at 35 weeks woke up one morning and realized she had not felt her baby move. She was not immediately worried. He had been active the night before. She had passed a non-stress test the day prior. She drank cold water, went outside, tried to coax movement through a work meeting. Nothing. So she went to the hospital, still convinced she was being overly cautious.

Within minutes of arriving, a doctor performing an ultrasound called a code OB. The room filled with nurses. Her clothes were pulled off. She barely had time to send a misspelled text to her partner before she was unconscious. Her son had no heartbeat. He was resuscitated for 18 minutes. He was transferred to a children’s hospital for therapeutic hypothermia -- a treatment that cools the brain to limit damage from oxygen loss. The MRI came back clean. He spent 35 days in the NICU and came home healthy.

She wrote about it months later, still processing the trauma, hoping the post would find another mother in time. It has been read thousands of times. In the comments, at least one reader went directly to the emergency room after reading it -- and was found to have a fetal arrhythmia.

Find our Interactive Fetal Movement Tool Here

ObGyn Intelligence: Safety analysis, the evidence critique, and the verdict are below -- for subscribers who want the full picture.

I guarantee that what follows will teach you how to save a live!

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She drives home alone.

That scenario is not unusual. It is the standard. And it is where the AI empathy debate is most practically consequential, because the gap that appointment leaves is now being filled by chatbots that answer questions at midnight, validate feelings in real time, and provide more emotional acknowledgment than most women receive in the clinical encounter itself.

The research finding that patients rate AI responses as more empathic than physician responses is not surprising in this context. [1] What it reveals is a compassion deficit so large that a language model can step into it and be experienced as better care. That is not a compliment to the technology. It is an indictment of what miscarriage care has become.

Empathy Is Not Enough

• Empathy is the perception of another person’s emotional state and some resonance with it. A clinician who acknowledges that a miscarriage is painful is demonstrating empathy. So is a well-trained chatbot.

• Compassion goes further: it is the commitment to act on what you perceive, to be present to the specific person in front of you, and to stay in that presence through the parts that are hard.

Miscarriage affects approximately 10 to 20 percent of known pregnancies, with higher rates in women over 35.

The majority have no identifiable cause. That last fact is one of the hardest things in medicine to communicate compassionately. Compassion means sitting with a woman in the fact that there is no explanation, no culpability, and no clear path to preventing it next time, and not retreating into the biology in order to manage your own discomfort.

What patients report needing after miscarriage is not primarily information. Studies on miscarriage experience consistently find that women want to feel that their loss was acknowledged as a loss, that the clinical team understood what this pregnancy meant to them, and that they were not processed through a protocol. Those are not communication preferences. They are descriptions of what compassionate care looks like.

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You no longer need an engineer to turn what you know into something patients can use.

“Tools will come and tools will go. Only the vibe coder remains.” A legendary music producer said it about software. It is also the best argument I know for why doctors should learn to build their own clinical tools. Here is what it means for medicine.

Andrej Karpathy, a founder of OpenAI, gave it a name in February 2025. Rick Rubin, who has produced more hit records than almost anyone alive, gave it a philosophy: “Tools will come and tools will go. Only the vibe coder remains.” Collins Dictionary named it the Word of the Year for 2025. Vibe coding has arrived.

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Most doctors are watching from the sidelines. They should not be.

What vibe coding actually is

Vibe coding means building software by describing what you want in plain language and letting an AI write the code. You do not memorize syntax. You do not hire an engineer. You say what the tool should do, the machine builds it, you test it, and you correct it. Karpathy’s original line was to forget that the code even exists. You see something, say something, run it, fix it.

Rubin’s point cuts deeper. He produces records without playing the instruments. His value is taste and judgment, knowing what good sounds like and steering the artist toward it. The tool in front of him keeps changing. His ear does not. That is the part that lasts.

For a doctor, the ear is clinical judgment

Fifty years at the bedside, thousands of patients, the feel for what a number means and when it matters. That is the scarce thing. Until now, turning that judgment into a working tool required someone else. You explained the idea to a programmer who did not know obstetrics, waited months, paid a great deal, and often got back something that missed the clinical point. The translation step was the bottleneck. Vibe coding removes it.

My own numbers

I have built more than 100 clinical tools this way. Risk calculators, screeners, patient education pages, decision aids, all on tools.obmd.com and liveevidence.com, all created by directing AI rather than writing code by hand. To my knowledge I am the only ObGyn in the United States with a tool library of this size built this way. I am in my seventies. I do not say this to impress you. I say it because if I can do it, the resident who grew up with a phone in her hand certainly can.

The tools are the easy part. What doctors get wrong about vibe coding is the part that actually matters.

The judgment is the product

A software engineer can build you a calculator. He cannot tell you whether the threshold is right, whether the population it was validated in looks like your patient, or whether the output will frighten a woman at two in the morning over a number that does not warrant it. You can. The clinical judgment is not a feature you add at the end. It is the whole point.

This is why doctors, not engineers, should hold the controls. The tool is only as good as the question behind it, and the question is clinical. When I build a tool, I decide what counts as a meaningful number. I write the plain-language explanation. I choose what the tool refuses to say because the evidence does not support saying it. Those are not coding decisions. They are medical ones.

Where vibe coding gets dangerous

Now the honest part. Karpathy’s advice to accept everything and forget the code exists is fine for a personal weekend project. It is reckless for a tool a patient will trust. A tool that computes a risk score is making a medical claim. It deserves the same scrutiny as a drug or a device.

So the rules are simple. Every number in the tool traces to a published source. I check the math against the original model, by hand, before anything goes live. I do not ship a black box: if a tool gives a result, it shows its reasoning and cites its evidence. And no patient data is ever stored or transmitted. My tools run entirely in the browser and keep nothing. Privacy is not a setting you switch on. It is the design.

Here is the paradox worth sitting with. The thing that makes a doctor safe at vibe coding is the same thing that makes vibe coding dangerous in untrained hands: judgment about what the numbers mean. A layperson who vibe codes a pregnancy risk tool does not know the threshold is wrong. You do. That is not a small advantage. It is the entire case for why this skill belongs to clinicians.

What this means for patients

When your doctor uses a tool to estimate your risk, you have a right to ask three things. Where do the numbers come from. Has the tool been checked against the published evidence. And what happens to the information you typed in. A good tool answers all three plainly. A tool built by a clinician who understands the evidence can answer them. A black box bought from a company often cannot.

The skill outlasts the tool

The tool I use today will be gone in two years. Karpathy himself, one year after coining the term, already calls vibe coding passe and renames the professional version agentic engineering. The label keeps changing. The brand of AI keeps changing. None of that matters. What Rubin understood, and what doctors should understand, is that the durable skill is not the tool. It is knowing what good looks like and being able to direct the machine toward it. For a doctor, that is simply clinical judgment pointed at a new instrument.

Conclusion

I am not telling you to become a programmer. I am telling you the opposite.

The era of the doctor as the person who memorizes ten thousand facts is ending, because the machine remembers better than any of us.

What it cannot do is care for the specific patient in front of you, or decide what a number should mean for her.

That is yours.

Vibe coding lets you build the tools that judgment deserves, without waiting for permission, a budget, or a programmer who has never set foot in a delivery room. Rubin reimagined the Tao Te Ching for the age of code. I will borrow from a source he would appreciate, Marcus Aurelius: the obstacle is the way. For doctors, the tool was never the obstacle. The translation was. It is gone. Build the thing only you can build.

ObGyn Intelligence is independent because readers keep it that way. If this changed how you think about the next decade of your work, subscribe. The evidence, and now the tools, should travel.

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References

1. Rubin R, Andreessen M, Horowitz B, Midha A, Torenberg E. Rick Rubin: Vibe Coding is the Punk Rock of Software. a16z Podcast. 2025. Available from: https://a16z.com/podcast/rick-rubin-vibe-coding-is-the-punk-rock-of-software/

2. Karpathy A. Post on X introducing the term “vibe coding.” February 2, 2025.

3. Collins Dictionary. Word of the Year 2025: vibe coding. 2025.

4. Taft DK. Vibe coding is passe. Karpathy has a new name for the future of software. The New Stack. February 10, 2026. Available from: https://thenewstack.io/vibe-coding-is-passe/

ObGyn Intelligence - Evidence Matters | obmd.com

Fertility medicine already runs on peptides. One of them is being oversold, and the mislabel has real consequences.

Fertility medicine already runs on peptides. The drugs that suppress or trigger ovulation in IVF, the hormones that grow the eggs, the brain signal that starts the whole cycle, all peptides. So when a weight-loss peptide starts bringing back ovulation in women who were not ovulating, it gets called a fertility drug. It is not one. And that mislabel can end in a surprise pregnancy on a medication that should be stopped before conception.

Polycystic ovary syndrome, or PCOS, is the most common hormonal cause of infertility. Many women with PCOS carry extra weight and insulin resistance, and that combination disrupts ovulation. GLP-1 drugs like semaglutide and liraglutide lower weight and improve insulin handling. When they do, ovulation can return. That is real, and for some women it is welcome. But improving the conditions that block ovulation is not the same as a drug designed to make you pregnant.

Here is what gets lost. A woman told her PCOS makes pregnancy hard, who then starts ovulating again on a GLP-1, can conceive without expecting to, while taking a drug not recommended in pregnancy. The fertility win and the medication risk arrive together. That is the part the marketing skips.

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A patient comes back for her second visit. Her symptoms have shifted. Her lab results are in. A good doctor remembers what happened last time, reads the new numbers, and adjusts the plan. That thinking over time is the hard part of medicine. The one-shot diagnosis is the easy part.

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A new study in Nature asked a simple question: can an artificial intelligence do the hard part?

AMIE stands for Articulate Medical Intelligence Explorer.

It is a medical AI system built by Google, running on their Gemini model. Earlier work showed it could take a patient history and suggest a diagnosis. This new paper pushes further, into what doctors call management — deciding what tests to order, what to prescribe, what to watch, and how to change course as a patient returns over weeks.

The design is clever, and worth understanding in plain terms. AMIE is built from two parts that work like the two speeds of human thinking.

• One part is a fast talker. It chats with the patient, sounds warm, and keeps track of the conversation.

• The other part is a slow thinker.

While the conversation continues, it quietly reads through hundreds of pages of clinical guidelines and writes a careful plan, with every recommendation tied to the exact guideline it came from. The idea comes straight from Daniel Kahneman’s Thinking, Fast and Slow: quick intuition on the surface, slow reasoning underneath.

To test it, the researchers ran a blinded, randomized exam. They wrote 100 made-up cases, each unfolding over three visits a day or two apart. The cases spanned five specialties, and obstetrics and gynecology made up 20 of the 100. Trained actors played the patients. For each case, the same patient was seen once by AMIE and once by one of 21 primary care doctors, all by text chat. Specialists then graded both, without knowing which one was the machine.

AMIE did at least as well as the doctors on overall management, and clearly better on two things. First, it was more precise. Instead of “prescribe an antibiotic,” it named the drug, the dose, the route, and the duration. On treatment precision it scored around 95 out of 100; the doctors scored in the 60s. Second, it stuck closer to the guidelines and showed its work, citing the source for each recommendation. When graders did prefer one over the other, they picked AMIE far more often than the doctor — though in about half the cases they saw no real difference at all.

Here is the part the headlines will skip. The doctors were set up to lose. They came from Canada and India but were judged against United Kingdom guidelines they do not use every day. They worked through a text box, not the phone or video they normally use. The cases had clean, correct answers, which real patients rarely provide. And the visits were one or two days apart, not the weeks the cases described — so human memory was tested in a way that does not match real care. AMIE, meanwhile, was built and tuned for exactly this setup. This is what I call the missing comparator. When you compare a system to people working with one hand tied, “the AI won” is not the finding. The finding is narrower, and still useful: the AI is very good at reading guidelines and writing precise plans.

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Why should an ObGyn care? One in five of these cases was ours. The things AMIE did best — remembering the last visit, citing the right guideline, writing a precise plan — are exactly the things that get sloppy on a busy, tired labor floor. A tool that never forgets and always cites the source has real value there. But the things AMIE cannot do are also ours: the woman in front of you whose values, fears, and body do not match the guideline. The study’s own authors say plainly that AMIE is not ready for real patients. They are right.

My take. This is careful, honest work, and I do not say that lightly about an AI paper. The two-speed design is smart, grounding the plan in guidelines is the right instinct, and the authors were more open about their limits than most. But “AI matches doctors” is not what this shows. It shows that AI beats a distracted human at the one thing computers have always been better at: holding everything in memory and quoting the manual exactly. That is useful. It is not judgment. For ObGyn the lesson is neither fear nor worship. Let the machine carry the memory and the guideline lookup, and keep the thinking about the specific woman where it belongs — with you. The real risk is not that AMIE replaces us. It is that we stop practicing the parts it does well and forget how to do them ourselves.

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One footnote, for the attentive reader. The study was funded by Google, and the authors may own Google stock. I mention this not as an accusation but as arithmetic.

AMIE is real, it is impressive, and it is a research demonstration, not a doctor. Read the result, not the headline.

References

1. Liévin V, Palepu A, Weng W-H, Saab K, Stutz D, Cheng Y, et al. Towards Conversational AI for Disease Management. Nature. 2026. doi:10.1038/s41586-026-10764-5.

2. Tu T, et al. Towards conversational diagnostic artificial intelligence. Nature. 2025;642:442–450.

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I want to close this series by asking a question that the health equity literature almost never asks directly: who benefits from the current policy response to Black maternal mortality?

I ask this not to impute bad faith to the researchers, advocates, and policymakers who are genuinely committed to addressing the problem. Many of them are. I ask it because the systematic preference for attitude-focused interventions over structure-focused interventions, in the face of evidence that points clearly toward structural mechanisms, is too consistent to be accidental. When a pattern is this consistent, there is usually a reason. Identifying the reason is not cynicism. It is analysis.

This series has established the following points with evidence:

The racial maternal mortality disparity persists across income levels, indicating that poverty is not the primary mechanism. Hospital of delivery accounts for nearly half the excess burden, indicating that institutional quality is a dominant variable. The insurance hierarchy routes patients away from faculty obstetric care through a reimbursement gradient that runs from commercial insurance through marketplace and union plans to Medicaid. The two-tier structure inside academic medical centers concentrates Medicaid and minority patients in resident clinics while faculty practices serve commercial patients. Continuity of care, which is structurally absent for patients in rotating-provider clinic systems, is associated with better outcomes for Black women specifically. Maternity care deserts have been created by the same reimbursement economics that drive urban faculty practice non-participation. Implicit bias training has not been shown to change clinical behavior or patient outcomes in any study. Doula care has not been shown to reduce maternal mortality or severe morbidity. The postpartum coverage cliff, now partially addressed by the 12-month extension, was responsible for a share of postpartum deaths that is computable from the mortality timing data.

Given this evidence base, a rational policy response would prioritize: raising Medicaid obstetric reimbursement rates to at least Medicare levels; requiring faculty practices at federally funded academic centers to accept Medicaid as a condition of NIH funding eligibility; mandating AIM quality improvement bundle implementation at hospitals receiving Medicaid disproportionate share payments; preserving rural obstetric unit capacity through enhanced reimbursement for low-volume high-necessity facilities; completing the national 12-month postpartum Medicaid extension in the remaining states; and strengthening continuity-of-care models in resident clinic settings.

None of these are the primary policy response. The primary policy responses have been implicit bias training mandates and doula Medicaid billing codes. The question is why.

ObGyn Intelligence: Safety analysis, the evidence critique, and the verdict are below -- for subscribers who want the full picture.

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Last week I went looking for one old document in ChatGPT. I found something I did not expect. There was a folder holding every file I had ever uploaded. Old drafts. PDFs. Documents I was sure I had deleted months ago. I had deleted the conversations. The files were still sitting there.

The folder is called the Library.

Most people do not know it exists.

It’s in your left sidebar.

In front of your eyes.

That is the whole problem in one sentence.

Here is what the Library is.

When you upload a file to ChatGPT, or when ChatGPT makes a file for you, it saves a copy. Documents, spreadsheets, pictures, PDFs, all of it. You do not turn this on. You do not click save. It happens on its own, quietly, in the background. The files pile up whether you notice or not.

Now here is the part that matters most. Deleting the chat does not delete the file. When you delete a conversation, it disappears from the list on the side of your screen. It looks gone. But the file you uploaded stays in the Library. So the thing almost all of us believe, that deleting a chat means it is gone, is simply not true. The chat is gone. The file is not.

Think about what people put into ChatGPT. A resume. A lease. A letter from a lawyer. A photo of a bill. A medical record. For my colleagues in medicine, think about every case we called de-identified that was not quite as anonymous as we told ourselves. Any of it can be sitting in storage you never knew you had, on a company’s computer, not yours.

• Opt-in means a person’s data is not used unless they actively give permission.

• Opt-out means the data may be used by default unless the person notices the setting and turns it off.

Opt-in is safer because it respects informed consent, reduces accidental data sharing, and places the burden on the organization to obtain permission before using personal information. Opt-out systems are weaker because many people never see the setting, do not understand it, or assume privacy is already protected.

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There is a second layer, and it is also turned on by default. ChatGPT has a setting called Improve the model for everyone. When it is on, the company can use what you type, and the files you upload, to train its future models. On by default. Most people never see the switch.

And when you finally do delete a file the right way, the company removes it from its servers within thirty days. So delete does not mean gone today. It means gone in about a month. That is better than forever. It is also not nothing.

So what do you do about it? Three things, and none of them take long.

1. First, turn off training. Click your picture in the top right corner of the screen. Open Settings, then Data Controls. Find the line that says “Improve the model for everyone” and switch it OFF. From that moment on, your new chats are not fed into the next model.

   

2. Second, clean out the Library in one go. Open the Library from the menu. At the top of the list of files you will see the word Name. Click it. A box appears that lets you select everything at once. Check it, pick what you do not want kept, and delete. You do not have to click each file one at a time, which matters when there are hundreds of them.

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3. Third, use Temporary Chat for anything private. The button sits in the upper right of the chat window. Looks like a broken circle. Not easy to find. A Temporary Chat is never saved to your Library and is never used for training. It is the right tool for the one-off question, the symptom you are embarrassed about, the document you do not want kept, the thing you would not say with the door open.
   Here it is, upper right of a new chat:

What does this mean for you?

Not that ChatGPT is dangerous (or maybe it is?).

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Saving your files so you can find them later is a fair and useful feature. For some.

The problem is not the Library. The problem is the silence around it. The saving is on by default. The deleting does not work the way the screen makes you think it does. And almost no one is ever told. That is not a technology problem. That is a consent problem.

Here is my take. I have spent fifty years telling patients the same thing. Consent only counts when it is informed. A choice you were never told about is not a choice you made. It is something that was decided for you while you were looking somewhere else. The rule that governs a procedure should govern your data too. You have a right to know what is being kept, what is being used, and how to stop it.

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So open your Library today. Look at what is in there. You may be surprised, the way I was. Then decide, with your eyes open, what stays and what goes.

It took me one click to find the folder. It took one more to empty it. The second click is the one worth telling people about.

If this saved you from leaving something private on a stranger’s server, it did its job. ObGyn Intelligence is where I write about exactly this, the gap between what we are told and what is true, in medicine and in the tools we now use every day. Subscribe, and bring the friend who still thinks deleting the chat deletes the file.

Sources

OpenAI. File storage and Library in ChatGPT. OpenAI Help Center. Available from: https://help.openai.com/en/articles/20001052-file-storage-and-library-in-chatgpt

OpenAI. What if I want to keep my history on but disable model training? OpenAI Help Center. Available from: https://help.openai.com/en/articles/8983130

The Stoics had a precise name for people who apply principles only when convenient. We should use it.

“Don’t explain your philosophy. Embody it.” -- Epictetus, Discourses

A woman is hemorrhaging. Her doctor knows exactly what she needs. The government has decided she cannot have it.

Somewhere else, a parent is told: the flu shot is a personal choice now. Between you and your doctor. Your body. Your decision. Medical freedom.

Same government. Same week. Same principle -- applied to one patient, denied to another.

The Stoics called this akrasia -- acting against one’s own stated reason. But Epictetus, who began life as a slave and understood power better than most philosophers ever would, had a harsher word. He called it moral cowardice dressed in borrowed language. You do not actually believe the principle. You use it. That is the definition of hypocrisy. And hypocrisy, to a Stoic, was not a personality flaw. It was a corruption of logos -- of reason itself -- the gravest failure a thinking person could commit.

The Words They Used

Let the record be precise, because precision is what this moment requires.

In October 2025, when the CDC moved COVID-19 vaccination from a blanket recommendation to “shared clinical decision-making,” HHS Deputy Secretary Jim O’Neill announced it this way: “Informed consent is back.” (1)

Secretary Robert F. Kennedy Jr. followed: “Government bureaucracies should never coerce doctors or families into accepting vaccines or penalize physicians for respecting patient choice. That practice ends now.” (2)

In December 2025, the White House ordered a restructuring of the entire childhood vaccine schedule -- from 17 recommended vaccines to 11. The stated reason: empowering families and clinicians to make individualized decisions.

Medical freedom.

Patient autonomy.

Shared decision-making.

Write those words down. We will need them.

What They Actually Did

In June 2025, this same administration rescinded the federal EMTALA guidance that had told hospitals across the country what every physician already knew: when a pregnant woman arrives in your emergency room and abortion is the medically necessary treatment to save her life, you treat her. (4)

EMTALA -- the Emergency Medical Treatment and Labor Act -- has been federal law since 1986. The Biden guidance did not invent new rights. It clarified existing law at a moment when clarification was desperately needed, because in the wake of Dobbs, physicians in states like Idaho, Texas, Arkansas, and South Dakota were watching patients deteriorate while they calculated their legal exposure.

That guidance is now gone.

What replaced it is silence. And in medicine, when the government goes silent while state laws threaten criminal prosecution for providing standard care, silence is a decision. It is a decision against the patient.

The same officials who declared “informed consent is back” have made it a crime to exercise it.

In Arkansas, Idaho, Mississippi, Oklahoma, and South Dakota, there are no meaningful health exceptions to abortion bans. A physician can watch a patient’s blood pressure climb toward stroke. She can watch a septic uterus fail to respond to antibiotics. She knows the treatment. And in those states, the government has already made the clinical decision for her. (5)

Doctors describe it now as “waiting for the patient to get sick enough.” Waiting until she crosses whatever invisible threshold might satisfy a prosecutor who was never in the room and who has never managed a hemorrhaging patient at 2 in the morning.

This is not shared clinical decision-making. This is the government making the clinical decision -- and threatening the physician with prison if she disagrees.

Marcus Aurelius on Consistency

“If it is not right, do not do it. If it is not true, do not say it.” -- Marcus Aurelius, Meditations IV.17

Marcus Aurelius wrote that in the 2nd century. He was Emperor of Rome. He had more power than any person today, and he chose to write about the discipline of consistency -- the obligation to mean what you say and to say only what you mean.

The Stoics called the alignment between word and deed homologia -- agreement, coherence, integrity in the literal sense of the word. To live with homologia was the goal of the philosophical life. To live without it was to live against nature, against reason, against everything that made human beings distinct from animals.

Patient autonomy is a real principle. Informed consent is a real principle. A principle is only a principle if it applies universally. The moment you decide it applies to flu shots but not to ectopic pregnancies, you have revealed that it was never a principle at all. It was a political instrument. And the Stoics would say you have not merely failed the patient. You have failed yourself.

The Surgeon General Nomination Explains Everything

The administration’s nominee for Surgeon General -- the nation’s doctor -- is Dr. Casey Means. She holds a medical degree from Stanford. She left her surgical residency six months before completion. Her medical license has been inactive since January 2024. She is a wellness influencer and supplement brand collaborator whose primary professional platform is social media. (6)

This is not an attack on Dr. Means. It is a statement of what this administration believes the nation’s doctor should look like.

The Surgeon General is the most visible public health communicator in America. She issues advisories. She shapes what the public believes about medicine. And this administration chose someone who has not practiced medicine in years, whose license is inactive, and whose credentials are a bestselling book and a podcast audience.

You do not nominate a practicing clinician who might apply evidence consistently. You nominate a communicator who will deliver the message you need delivered -- even when the message contradicts itself from one medical procedure to the next.

Vaccine autonomy for parents who may choose to decline. Criminal prosecution for physicians who choose to save a pregnant woman’s life. Both positions, from the same office, in the same year.

What Epictetus Would Say

“Seek not that the things which happen should happen as you wish; but wish the things which happen to be as they are, and you will have a tranquil flow of life.” -- Epictetus, Enchiridion VIII

That is not the Epictetus I want to invoke here. I want the Epictetus of the Discourses -- the former slave who had seen what it looked like when powerful people used beautiful language to justify cruelty.

He wrote that the hypocrite is the most dangerous person in any community -- not because he does evil, but because he does evil while speaking the language of good. He confuses everyone around him. He makes it harder to name what is actually happening. He borrows the moral authority of real principles and spends it on their opposite.

“First say to yourself what you would be,” Epictetus wrote, “and then do accordingly.” This administration has said what it would be. Patient choice. Medical freedom. Informed consent. It has not done accordingly. It has done the opposite, to the patients who needed those principles most.

That is not a policy difference. That is not a values disagreement. That is a failure of logos -- of reason, of consistency, of basic human integrity.

Epictetus called it exactly what it is. We should too.

What Physicians Must Understand Right Now

If you practice obstetrics in a state with a near-total abortion ban, you are operating in deliberately manufactured legal ambiguity. The federal guidance that clarified your EMTALA obligations is gone. The federal government has no current enforcement position. Your state law may expose you to criminal prosecution for providing treatment that would be standard of care in 37 other states.

That ambiguity was not an accident. It was a choice. The administration that eliminated it had the option to clarify it in the other direction. It chose not to.

Document everything. Know your hospital’s legal counsel. Know your state’s specific exceptions, narrow as they are. And know this: the physician who hesitates while a patient hemorrhages because she is calculating her legal exposure is being asked to be something no physician should ever be asked to be -- a defendant deciding whether to treat.

What Women Must Understand Right Now

The administration using the phrase “between you and your doctor” is not applying that principle to you if you are pregnant.

If you live in a state with an abortion ban and you develop a life-threatening complication of pregnancy, the decision about your care is not between you and your doctor. It is between your doctor and a prosecutor. Your autonomy -- the autonomy this administration invokes loudly in every vaccine press release -- does not reach the emergency room when you need it most.

Know your state’s laws before you need them. Know which hospitals have the strongest legal and ethics support. Know that EMTALA still exists as federal law even without the Biden guidance -- the statute has not changed, only the enforcement posture. If you are denied emergency care you believe was medically necessary, contact the Center for Reproductive Rights, the ACLU Reproductive Freedom Project, or your state’s medical board. Document everything. You have rights that have not been abolished, only obscured.

“Waste no more time arguing what a good man should be. Be one.” -- Marcus Aurelius, Meditations X.16

This administration has wasted considerable time telling us what a patient-centered health system should sound like. It has not been one.

Medical freedom is not a slogan. Patient autonomy is not a press release. Informed consent is not a political instrument to be deployed against vaccines and withheld from pregnant women bleeding in emergency rooms.

Either the principle applies to every patient, or it is not a principle.

Either the doctor-patient relationship is sacred, or it is not.

You cannot have it both ways. You cannot invoke medical freedom at a podium and then criminalize the exercise of clinical judgment in an operating room.

The Stoics understood that a person’s character is revealed not by what they say when it is easy, but by what they do when the principle costs them something. This administration has told us exactly who it is.

We should believe it.

References

1. O’Neill J. CDC Immunization Schedule Adopts Individual-Based Decision-Making for COVID-19. HHS Press Release. October 6, 2025.

2. Kennedy RF. Post on X (formerly Twitter). December 31, 2025.

3. White House Fact Sheet. President Donald J. Trump Begins Process to Align U.S. Core Childhood Vaccine Recommendations with Best Practices from Peer, Developed Countries. December 7, 2025.

4. Center for Reproductive Rights. Trump Administration Attacks Federal Protections for Pregnant People in Emergency Rooms. June 3, 2025.

5. PBS NewsHour. White House revokes guidance requiring hospitals to provide emergency abortions. June 5, 2025.

6. Wikipedia. Casey Means. Accessed February 2026.

7. Epictetus. Discourses. Trans. Oldfather WA. Harvard University Press; 1926.

8. Aurelius M. Meditations. Trans. Hays G. Modern Library; 2002.

9. [VERIFY] -- U.S. maternal mortality comparative data vs. peer nations. CDC/WHO.

Two pathologists look at the same breast biopsy. One sees cancer or another anomaly. One does not. This is not a thought experiment. In a 2015 study in JAMA, 115 pathologists read the same slides. Across the full spectrum of breast findings — benign, atypia, DCIS, invasive — overall agreement with the expert panel was 75 percent, with the disagreement concentrated in the preinvasive gray zone.” The cases that mattered most — the early, borderline changes — were the ones they disagreed on most. Same slide. Same patient. A different answer depending on who held the microscope.

I keep that number in mind whenever someone tells me medical AI cannot be trusted.

A new editorial in the International Journal of Medical Informatics makes a strong claim right in its title: almost all machine learning models for medicine are wrong. The authors are serious people, and part of their argument is right.

The labels used to train these tools come from human experts who disagree.

The models are tested on the same kind of patients they learned from, not on new hospitals or new populations. They are scored with numbers that look exact but shift the moment the setting changes. And once switched on, they are rarely watched, even though medicine keeps changing underneath them.

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The title is not new.

It echoes a famous 2005 essay, “Why most published research findings are false.” That one was about human research — the same studies and expert opinions these models are trained to copy. Follow its logic and almost all of medicine rests on shaky findings too. Somehow the cannon gets pointed only at the machine.

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All true, as far as it goes. I have made versions of these points myself. Build a tool on a weak foundation, never test it in the real world, and you should not trust it. But the paper never asks the one question that matters most. Wrong compared to what?

Every flaw on their list is also a flaw in human doctors — usually a bigger one. Doctors disagree with each other, as those pathologists did.

They are trained once, tested once, and then practice for decades. Nobody runs a doctor through a second hospital to confirm she still performs. Nobody measures, year after year, whether her judgment still matches the evidence. And the knowledge she learned in training is often more than a decade behind the best current science — on average it takes about 17 years for research to reach everyday practice. A machine can be corrected overnight. A habit takes a generation.

So when a study calls a model “wrong,” I want to know: wrong next to what human standard?

The human standard is not perfect truth.

It is a group of people who disagree, carry old knowledge, and are almost never checked.

This is where the paper trips over its own feet. Its strongest section explains that there is no clean truth in medicine. Diagnoses are fuzzy. Lab tests vary. Experts split. The authors say so plainly. Then they turn around and call the machine “wrong” — wrong against the same fuzzy standard they just told us not to trust.

You cannot have it both ways. If the ruler is made of rubber, the word “wrong” loses its meaning. When a model disagrees with a label, it often disagrees with a label a second expert would have rejected too. That is not error. That is the ordinary disagreement of medicine, renamed as a machine defect.

So who is the arbiter of truth? In medicine, there isn’t one. There is a committee of fallible humans, and we have agreed to treat their majority vote as the truth. That is fine — as long as we judge the machine by the same yardstick we use on ourselves, not a stricter one we invented the day the machine walked in.

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I will give the authors one real point: scale. One tired doctor harms patients one at a time. A bad model, switched on across a whole hospital system, can harm thousands before lunch. That is a genuine reason to watch deployed tools closely — to test them, keep them honest, and check them over time. Their list of fixes is a good list. I would sign most of it. But that is an argument about how we deploy these tools, not about whether they are “wrong.” The methods in this paper are right. The frame is a double standard wearing a white coat. We are demanding proof from the machine that we never demanded from ourselves.

My position is simple.

Hold medical AI to a high standard.

Then hold human medicine to the exact same one.

Stop asking whether the model is perfect.

Start asking whether it is better than what we already do — and then actually measure what we already do, which we almost never have. A tool does not need to be flawless. It needs to beat the disagreeing, aging, unchecked standard we already live with. Often, it already does.

The next time you read that almost all medical AI is wrong, ask the question the headline skips: compared to whom?

Share this with a colleague who still calls the human exam the gold standard — the data on how often we agree should travel.

ObGyn Intelligence is free because the work matters. If you want to keep it independent and in your inbox, subscribe.

References

1. Cabitza F, Jurman G, Molinari F, Bellazzi R. Why almost all ML models for medicine are wrong — and what we need for evidence-based medical AI. Int J Med Inform. 2026 (in press). doi:10.1016/j.ijmedinf.2026.106538.

2. Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA. 2015;313(11):1122-1132. doi:10.1001/jama.2015.1405. PMID 25781441.

3. Morris ZS, Wooding S, Grant J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med. 2011;104(12):510-520. doi:10.1258/jrsm.2011.110180. PMID 22179294.

4. Ioannidis JPA. Why most published research findings are false. PLoS Med. 2005;2(8):e124. doi:10.1371/journal.pmed.0020124. PMID 16060722.

The proven end of the spectrum: the peptides that run labor and delivery, and the open questions behind each one.

Oxytocin is one of the oldest and most-used drugs in obstetrics. It starts labor, it strengthens labor, and it stops the bleeding that still kills mothers. It is also classed as a high-alert medication, a category for drugs that can cause serious harm when the dose runs ahead of the evidence.

The delivery room runs on peptides. Three matter most. Oxytocin, the workhorse. Carbetocin, a longer-acting version built for places where refrigeration fails. And atosiban, a peptide that does the opposite job, relaxing the uterus to hold off preterm labor. All three are real drugs with real trials. That does not mean the questions are settled.

Here is the part patients are rarely told. After more than 50 years of using oxytocin, obstetrics still does not agree on the dose. The longer-acting option works about as well as the original and is not a magic upgrade. And the uterus-relaxing peptide that is standard across Europe was turned down by the FDA. These are not fringe debates. They shape what happens to a laboring woman and her baby.

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Ask any AI chatbot a medical question and you get an answer in seconds. It sounds calm, organized, and sure of itself. That last part is the problem. Confidence is not the same as reliability, and a chatbot has no way to tell you which of its sentences it would stake its life on and which it is simply guessing.

So let me show you something simple. I asked a medical AI one ordinary clinical question — the kind a pregnant woman or a first-year resident might ask. Then I asked it the same question a second time, word for word, with a single instruction added to the end. Same model. Same minute. Two very different answers.

Here is the first one.

Prompt: “Tell me what blood tests a pregnant woman should do at her first prenatal visit.”

Here is the answer:

“At her first prenatal visit, a pregnant woman should have her blood type and Rh factor checked, along with an antibody screen and a complete blood count to look for anemia. She should be tested for rubella immunity, hepatitis B, hepatitis C, HIV, and syphilis, and screened for chlamydia and gonorrhea. Her thyroid should be checked with a TSH test, and she should be offered genetic carrier screening and screening for Down syndrome. A urine test is also part of the standard first visit.”

Not wrong, exactly. A reasonable list. But notice what it does not do. It never separates the tests every pregnant woman gets from the ones that depend on her age, weight, or history. It presents a thyroid test as routine — when the main US guideline actually recommends thyroid testing only for women with specific risk factors. It cites nothing. And it never once tells you what it would need to know about the patient to answer well. It just talks.

Now the same question, with one instruction added to the end.

That instruction is the Clinical Evidence Prompt or the CEPrompt™.

PROMPT: “(Tell me what blood tests a pregnant woman should do at her first prenatal visit [PLUS THE INSTRUCTION, the CEPrompt™.”

Here is the full answer it produced — nothing trimmed.

“This is the US/ACOG framework at a routine first visit, assuming no results already documented this pregnancy.

Universal — standard of care for all (high certainty): blood type, Rh(D) and antibody screen; CBC; rubella IgG; hepatitis B (HBsAg minimum, triple panel if not previously documented); hepatitis C (every pregnancy); HIV (opt-out); syphilis (repeat in third trimester and at delivery). Urinalysis and urine culture are also routine but not blood-based.

Selective — depends on the individual patient (moderate certainty): chlamydia/gonorrhea (universal if under 25, otherwise risk-based); varicella IgG if no immunity history; hemoglobinopathy evaluation guided by CBC/MCV and ancestry; early diabetes screening if overweight/obese with risk factors or prior GDM; lead and TB testing if at risk.

Offered, not assigned: genetic carrier screening and aneuploidy screening (cell-free DNA or serum) — both offered to all regardless of age.

Evidence vs. position: The universal panel reflects guideline consensus. Thyroid (TSH) is the exception — ACOG (PB 223) recommends selective, risk-based testing, not universal. Presenting routine TSH as standard is an inference, not consensus; universal offering is a defensible minority position, not the guideline.

Missing information that changes the selective list: maternal age, BMI, ancestry, prior obstetric/GDM history, immunization and prior-testing records, STI risk, gestational age at presentation.

Certainty: high for the universal panel; patient-dependent for selective tests; the thyroid point is genuinely contested.

Citations: Viral Hepatitis in Pregnancy. Clinical Practice Guideline No. 6. ACOG. Obstet Gynecol 2023;142:745–59. SMFM Consult Series #69: Hepatitis B in pregnancy. Am J Obstet Gynecol 2024;230:B2–B11. Owens DK, et al. USPSTF hepatitis C screening. JAMA 2020;323:970–5. doi:10.1001/jama.2020.1123.”

Read that again, because every line of it is doing a job the first answer skipped. It splits the tests into three groups instead of one: the ones every pregnant woman gets, the ones that depend on her age, weight, ancestry, or history, and the ones that are offered for her to accept or decline. It draws a clear line between guideline consensus and the author’s own position — and it catches the thyroid trap, telling you plainly that routine TSH for everyone is not what the main US guideline recommends, even though the first answer presented it as routine. It tells you what it would need to know about the actual patient before the selective list can be finalized. It labels how certain it is, group by group. And it ends with real citations you can open and check, not a vague wave at “the guidelines.”

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That is the difference between a list and a consultation. The first answer hands you tests. The second one reasons with you — it shows what is settled, what depends on the patient, what is contested, what is missing, and where it got each claim. A clinician can act on the second answer. A patient can ask better questions because of it. Nobody should act on the first.

Same machine. The model did not get smarter between the two answers. The second answer is better because the prompt told it to show its work — to separate what the evidence supports from what it is assuming, to say how sure it is, to name what it is missing, and to admit when something is genuinely debated.

That instruction is the whole trick.

That instruction is the Clinical Evidence Prompt or the CEPrompt™.

It is one sentence.

You paste it at the end of any clinical question, and it changes the kind of answer you get back. Not the topic. The honesty.

The exact wording of the CEPrompt™ — the sentence that turns the first answer into the second — is below, with the six things it does and how to attach it to any question you ask. Paid subscribers read on.

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A pregnant woman recently posted a simple question to a parenting forum.

Everyone warned her about labor, she wrote. Nobody talked about after. What actually happens to your body and brain in those first weeks? More than four hundred women answered. The replies were remarkably consistent, and they read like a clinical review nobody had handed her.

That is the problem in one sentence.

We prepare women intensively for labor, which lasts hours, and we barely prepare them for recovery, which lasts months. The result is that women meet predictable, normal changes with terror, and sometimes miss the rare changes that are genuinely dangerous. Both failures trace back to the same gap: no one gave them the briefing in advance.

What the Fourth Trimester Actually Is

The fourth trimester is the first twelve weeks after birth. Your body spent forty weeks building and supporting a pregnancy. It does not reverse that overnight. Hormones that were sky-high for months fall within days. The uterus, stretched to the size of a watermelon, shrinks back to the size of a pear. Blood volume that increased by almost half during pregnancy has to come back down. Tissue that stretched or tore has to heal. Milk production switches on. All of this happens while you are sleep-deprived and caring for a newborn.

This is not a side note to pregnancy care. It is half of it. The American College of Obstetricians and Gynecologists now calls these weeks the fourth trimester and treats postpartum care as an ongoing process rather than a single six-week checkup. The reason is blunt: more than half of pregnancy-related deaths in the United States happen after the birth, not during it.

ACOG recommends that every woman have contact with her doctor or midwife within the first 3 weeks after delivery, and a full comprehensive visit no later than 12 weeks¹. Most women still get one visit at six weeks and nothing before it. That schedule was built around convenience, not around when problems actually occur.

Why This Matters

When researchers asked mothers, obstetricians, and nurses to rank what recovery actually involves, the highest-ranked domain was not pain or physical healing. It was psychosocial distress: the emotional and mental load of the postpartum period.

That finding² matches what women report over and over. The single most surprising part of recovery is rarely the stitches. It is the sudden emotional crash, the bleeding that will not stop, the bowel movement they dread, and the breastfeeding that does not come naturally. None of these are secrets in medicine. They are simply not being said out loud, in advance, with numbers and a timeline.

Here is the briefing. Read it now, while you are still pregnant, so that when day four arrives it feels like a forecast instead of an ambush.

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The free section above covers what the fourth trimester is and why it matters. The full playbook below covers each change in turn, the exact warning signs and numbers, and how to prepare before the baby arrives.

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Summary

Two sentences. That is all it took. An L&D nurse posted to r/pregnant: “PLEASE do not home birth. Just go to the hospital and refuse everything if you don’t want any interventions.”

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The post collected 3,100 upvotes and 291 comments before it was locked. It had clearly been written in grief — the top comment, itself upvoted 3,000 times, read simply: “I’m guessing this came out of a sad, traumatic experience at work this weekend. I’m so sorry.” What followed was one of the more clinically dense comment threads I have seen on this platform. Nearly every major obstetric emergency made an appearance: placental abruption, cord prolapse, PPH, shoulder dystocia, meconium aspiration, uterine atony, preeclampsia.

Each story carried the same structure — textbook pregnancy, routine labor, then something that required a surgeon in the room within minutes.

A minority of commenters pushed back, raising international data on midwife-integrated home birth in Australia, the Netherlands, and Canada. The thread is messy, emotional, and largely correct — but it misses the most important number in the conversation.

The rest of this post is for paid subscribers.

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Here is the problem.

Vitamin D is barely a food vitamin at all. Your body was built to make it in your skin from sunlight, not to pull it off a plate. Very few foods carry a useful amount. The richest is oily fish. A 3.5-ounce piece of wild salmon gives you somewhere around 570 to 650 units. Farmed salmon gives less, often closer to 440. Mackerel runs high, near 640. Canned sardines sit around 190, herring around 170. After that, the list falls off a cliff. An egg yolk has about 40 units. A glass of fortified milk has about 100. Most other foods have almost none.

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Now set that against what an adult needs each day: about 600 units under age 70, and 800 after.

To reach that with food alone, you would need oily fish on your plate nearly every single day. Most people eat fish twice a week if they are trying. And the fish itself is unreliable — wild salmon can carry three or four times the vitamin D of farmed, and you rarely know which one is on your fork. So for the average person, food does not get there. That part is not up for debate. It is arithmetic.

Sunlight is the natural answer, and for most of human history it was the only one. But modern life works against it. If you live in the northern half of the country, your skin makes almost no vitamin D from October to March. Sunscreen blocks it, which is the right trade when the other option is skin cancer. Office work blocks it. Darker skin needs more sun to make the same amount. Age cuts production too. So the natural source is real, but for most people it is not enough.

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This is where the story gets interesting, because the supplement world ran ahead of the evidence. For years the message was that everyone should take high doses, and that more was always better. Then the large trials arrived. VITAL, a study of more than 25,000 American adults, tested 2,000 units a day against a dummy pill. It did not lower cancer. It did not lower heart attacks or strokes. A companion study found it did not prevent broken bones either. In 2024 the Endocrine Society read all of this and made a quiet, sensible call: most healthy adults under 75 do not need to supplement beyond the basic amount, and most do not even need a blood test to check their level.

That sounds like an argument against the pill. It is not. It is an argument against the hype, and against treating a deficiency drug like a daily ritual for people who are already fine. Because the same trials, read honestly, show exactly who does benefit.

Pregnant women sit at the top of that list, and this is my field. The Endocrine Society recommends vitamin D in pregnancy because it is tied to lower rates of preeclampsia, preterm birth, babies born too small, and newborn death. Those are not small things. Children benefit too — vitamin D prevents rickets and lowers respiratory infections. Adults over 75 have a lower risk of death. And there is one finding that matters especially for women: in VITAL, vitamin D cut the rate of new autoimmune disease by about 22 percent — conditions like rheumatoid arthritis, thyroid disease, and psoriasis. The protection held up over seven years of follow-up, with 156 cases on vitamin D against 198 on placebo. Women develop autoimmune disease far more often than men, so this benefit lands harder for them. Then add the simplest case of all: anyone whose level is genuinely low. In VITAL, about 1 in 8 adults started out deficient, and another third were borderline. Fixing a true deficiency is not hype. It is repletion.

So what do I tell patients now?

Diet will not carry this nutrient for most people. Eat the oily fish and the fortified foods, get safe sun when you can, but do not expect your plate to close the gap. Then match the pill to the person, and to a real number.

For a healthy adult, the baseline target is 600 IU a day up to age 70, and 800 IU after that. If you are pregnant, aim higher: the trials behind the recommendation used 600 to 5,000 IU a day and averaged about 2,500, so a daily supplement of 1,000 to 2,000 IU, on top of the vitamin D already in your prenatal, is a sound and common target.

Children aged 1 to 18 do well on about 1,200 IU a day, and a baby under one needs 400 IU. If your blood level is genuinely low, 1,000 to 2,000 IU a day will usually bring it back up. If you are a healthy 40-year-old man with a normal level, you do not need to chase a higher number, and you should stay under the adult ceiling of 4,000 IU a day, because very high doses carry their own risk of harm.

More is not better here. Enough is better.

One honest caveat, because it cuts the other way. The big no-benefit trials enrolled mostly people who already had enough vitamin D. So no extra benefit may really mean no benefit on top of enough. That is a fair criticism, and it is exactly why I aim at deficiency rather than at the supplement aisle.

My take: this is not a debate about whether vitamin D is essential. It is. The real question is how you get it and who needs help getting it.

Food alone rarely gets anyone to the target.

Sunlight is unreliable in modern life.

And a cheap daily pill closes the gap for the people the evidence actually points to — pregnant women first among them. Replete the deficient, protect the pregnant, and stop megadosing the healthy.

Bottom line: skip the megadose, not the basics. If you are pregnant, older, or low, take the small daily dose — your dinner plate cannot do this job. If this helped you think more clearly about what is on your counter, subscribe to ObGyn Intelligence. The evidence here is better than the marketing, and it stays that way.

References

1. Manson JE, Cook NR, Lee IM, Christen W, Bassuk SS, Mora S, et al. Vitamin D supplements and prevention of cancer and cardiovascular disease. N Engl J Med. 2019;380(1):33-44. doi:10.1056/NEJMoa1809944. PMID 30415629.

2. LeBoff MS, Chou SH, Ratliff KA, Cook NR, Khurana B, Kim E, et al. Supplemental vitamin D and incident fractures in midlife and older adults. N Engl J Med. 2022;387(4):299-309. doi:10.1056/NEJMoa2202106.

3. Hahn J, Cook NR, Alexander EK, Friedman S, Walter J, Bubes V, et al. Vitamin D and marine omega-3 fatty acid supplementation and incident autoimmune disease: VITAL randomized controlled trial. BMJ. 2022;376:e066452. doi:10.1136/bmj-2021-066452. PMID 35082139.

4. Costenbader KH, Cook NR, Lee IM, Hahn J, Walter J, Bubes V, et al. Vitamin D and marine n-3 fatty acids for autoimmune disease prevention: outcomes two years after completion of a double-blind, placebo-controlled trial. Arthritis Rheumatol. 2024;76(6):973-983. doi:10.1002/art.42811. PMID 38272846.

5. Demay MB, Pittas AG, Bikle DD, Diab DL, Kiely ME, Lazaretti-Castro M, et al. Vitamin D for the prevention of disease: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2024;109(8):1907-1947. doi:10.1210/clinem/dgae290.

I monitor dozens of communities for clinically meaningful posts: dangerous myths, gaps between belief and evidence, stories guidelines cannot capture. This series -- ObGyn Intelligence on Reddit -- dissects them against the literature, because ObGyns who ignore social media ignore the most unfiltered window into what patients think, fear, and do between appointments.

Summary

A pregnant Redditor asked a simple question and got more than a thousand answers: does anyone still eat sushi, smoked salmon, and deli meat? The overwhelming reply was yes, in moderation, from places they trust.

What stood out was how many women pushed back on the official warnings.

Again and again, commenters noted that bagged salads, pre-cut melon, and cantaloupe cause more listeria outbreaks than any sandwich, yet nobody tells pregnant women to stop eating salad.

A few said they avoid everything, often after a loss or years of trying to conceive, and the thread met that choice with real tenderness. But the question hides a problem, and so does the advice these women were given. It lumps three very different foods together, as if the risk were the same for all of them. It is not, and the body issuing that advice knows it is not.

Three Foods, Three Different Risks

These foods belong in one sentence as risks for Listeria.

Counter-sliced deli meat qualifies: the slicer itself can carry listeria, which is why warming the meat until it steams removes most of the risk.

Cold-smoked salmon qualifies too, and more so; smoking does not reliably kill listeria, and the product is stored cold and eaten as is. One German genomic study estimated that smoked or cured salmon accounted for more than a quarter of that country’s listeriosis cases between 2018 and 2020. Of the three foods named, the smoked salmon is the real listeria concern.

Liusteria does not belong here.

Listeria monocytogenes is a soil and water organism that survives refrigeration and is killed only by cooking or pasteurization, so the genuine danger lives in refrigerated, ready-to-eat foods that sit cold for days with no further heating.

Fresh raw-fish sushi is the outlier. In the federal modeling that traces more than three-quarters of U.S. listeriosis to dairy, leafy greens, and fruit, fresh fish and other seafood account for essentially none of it. Sushi is served quickly, not held cold for days, so listeria has little chance to multiply, and it has not been implicated in U.S. listeria outbreaks. Its real risks are different: the Anisakis parasite, which the FDA’s mandatory deep-freezing of sushi-grade fish all but eliminates; ordinary stomach bugs such as salmonella that make the mother ill but rarely reach the fetus; and methylmercury, which is a matter of fish species, not of raw versus cooked.

What ACOG Says — and Why It Is Wrong

This confusion is not invented by patients. It is printed in the patient materials of the American College of Obstetricians and Gynecologists, so it is worth reading their exact words. On the page ACOG titles “Listeria and Pregnancy,” the instruction is unambiguous:

“Do not eat sushi made with raw fish (cooked sushi is safe).”

— ACOG, “Listeria and Pregnancy” FAQ

The placement is the whole problem. This sentence sits on a page about listeria, and the same page tells the reader that cooking and pasteurization are the only ways to kill the bacterium. Read together, those two statements tell a pregnant woman that raw-fish sushi transmits listeria. It does not.

ACOG has reached for the right bacterium and attached it to the wrong food.

In its general “Having a Baby” guidance, ACOG offers this:

“Raw fish may be harmful during pregnancy.”

— ACOG, “Having a Baby” FAQ

“May be harmful” is the language of liability, not of risk communication. It carries no magnitude, no mechanism, and no comparison. It cannot be acted on and it cannot be proven wrong, which is precisely why it survives in a guideline. A patient deserves a number and a reason, not a phrase engineered to be unfalsifiable.

And in its dedicated sushi FAQ, ACOG explains that raw fish, including sushi and sashimi, are:

“more likely to contain parasites or bacteria than fully cooked fish.”

— ACOG, “Can I eat sushi while I’m pregnant?” FAQ

That single phrase fuses three separate hazards into one alarming blur.

Pull them apart and the case collapses.

The parasite, Anisakis, is largely engineered out by the FDA-mandated freezing of any fish sold for raw consumption.

The bacteria that raw fish can actually carry are salmonella and campylobacter, which cause maternal stomach illness and, in the United States, do not behave like listeria and rarely reach the fetus. Listeria, the one organism that genuinely threatens the pregnancy, is the bug that raw sushi is not known to carry. Lumping these together lets the most frightening member of the group lend menace it has not earned here.

Laid out plainly, the failures are these:

1. Right bug, wrong food. ACOG invokes listeria to justify avoiding sushi, but fresh raw fish is not a documented listeria vehicle and contributes essentially none of the attributed U.S. cases.

2. Hedged, unquantified language. “May be harmful” protects the author from liability while giving the patient nothing she can weigh or act on.

3. Conflated hazards. Parasites, enteric bacteria, and listeria are merged into “parasites or bacteria,” obscuring that the dangerous one is absent and the present ones are mild and freezing-controlled.

4. Inverted risk hierarchy. The warning is loudest on salmon nigiri and quietest, in the same patient breath, on the foods that actually drive listeriosis: cantaloupe, bagged salad, pre-cut melon, soft cheeses, deli meat, and smoked fish.

5. The wrong sorting variable. ACOG sorts seafood on raw-versus-cooked. But mercury tracks fish species, not preparation, and listeria tracks refrigerated ready-to-eat shelf life, not rawness, which is why cooked-but-cold smoked salmon is a real listeria vehicle and raw nigiri is not.

To be fair to the kernel: ACOG is correct that pregnant women are roughly ten times more susceptible to listeriosis, that the consequences can be catastrophic, and that avoiding raw and undercooked seafood does reduce salmonella and parasite exposure.

A simple, conservative rule has value across a large and varied population. The problem is not that the advice is cautious. It is that it is mis-aimed, conflated, and unquantified, and it spends its credibility on the wrong target while staying quiet on the foods that fill the morgue.

What To Actually Avoid: The Listeria List

Here, explicitly, are the foods that transmit Listeria monocytogenes, grouped from highest concern down. The bacterium grows in the refrigerator and dies only with thorough cooking or pasteurization, so the through-line is simple: refrigerated, ready-to-eat, no kill step before the mouth.

1. Dairy (the largest single category)

2. Refrigerated ready-to-eat meats

3. Refrigerated smoked and ready-to-eat seafood

4. Produce (the under-recognized vehicles)

5. Refrigerated prepared and store-deli foods

Here they are:

1. Dairy (the largest single category)

• Unpasteurized (raw) milk and anything made from it

• Soft cheeses unless labeled made with pasteurized milk: queso fresco, queso blanco, panela, cotija, brie, camembert, feta, and blue-veined cheeses

• Any soft cheese under an active recall, even pasteurized (recent brie and camembert outbreaks)

2. Refrigerated ready-to-eat meats

• Deli and luncheon meats and cold cuts, especially counter-sliced, unless heated until steaming (165°F)

• Hot dogs straight from the package, unless heated until steaming

• Refrigerated pâté, meat spreads, and liverwurst

• Refrigerated pre-cooked chicken and ready-to-eat prepared meals, unless reheated

3. Refrigerated smoked and ready-to-eat seafood

• Cold-smoked salmon (lox, nova), gravlax, smoked trout, smoked whitefish, unless cooked or in a fully heated dish

• Refrigerated ready-to-eat crab, surimi, and pre-cooked shrimp, unless heated

4. Produce (the under-recognized vehicles)

• Pre-packaged and bagged salads and pre-cut leafy greens

• Pre-cut and pre-packaged fruit, especially melon; cantaloupe caused the deadliest U.S. listeria outbreak on record

• Raw sprouts of all kinds (alfalfa, bean, clover, radish); rinsing does not decontaminate them

• Enoki and other raw mushrooms implicated in repeated recalls

5. Refrigerated prepared and store-deli foods

• Store-made deli salads: chicken, ham, egg, tuna, and seafood salads

• Refrigerated hummus and dips during active recalls

• Pre-packaged hard-boiled eggs (recall history)

Across every category

• Anything currently under an active FDA or USDA listeria recall — check before assuming a food is safe

• Anything kept past its use-by date in the refrigerator, since listeria multiplies in the cold and time is the real risk multiplier

The rule patients should carry out of the office: refrigeration does not kill listeria. Only cooking or reheating to steaming (165°F) and pasteurization do. Keep the refrigerator at or below 40°F. And note what is absent from this entire list: fresh raw-fish sushi, the very food ACOG warns against on its listeria page.

What It Means

For patients, the risk is real but small, and it is not spread evenly across the menu. The precautions that move the needle are the unglamorous ones on the list above: wash produce, skip the bagged salad and pre-cut melon, heat deli meat until it steams, and choose smoked fish from a trusted source or warm it through. Raw-fish sushi from a reputable restaurant sits far down the list of things worth losing sleep over. For clinicians, the lesson is about credibility. Patients now read the same outbreak data we do. When we hand them a guideline that bans salmon nigiri but stays silent on cantaloupe, they learn that the list is about caution and liability, not about their baby, and that costs us the trust we need for the warnings that matter.

My Take

I have spent fifty years inside this system, and I have read a lot of these handouts. ACOG put sushi on a page about listeria, a bacterium that sushi does not carry, and told mothers that raw fish might be harmful without ever saying how, how often, or compared to what. That is not science. That is a phrase written to survive a deposition. Meanwhile cantaloupe, which caused the deadliest listeria outbreak this country has ever seen, rarely earns a sentence. There is something almost poetic about a woman trusting a sushi chef with twenty years at the same counter over a deli slicer cleaned, if she is lucky, once a day. She is reasoning better than the pamphlet. So tell her the truth, all of it: skip the bagged salad, heat the ham, and enjoy the salmon nigiri. A calmer pregnancy is worth more than a list nobody can defend.

Sources

1. American College of Obstetricians and Gynecologists. Listeria and pregnancy (FAQ). https://www.acog.org/womens-health/faqs/listeria-and-pregnancy

2. American College of Obstetricians and Gynecologists. Having a baby (FAQ). https://www.acog.org/womens-health/faqs/having-a-baby

3. American College of Obstetricians and Gynecologists. Can I eat sushi while I’m pregnant? (Ask ACOG). https://www.acog.org/womens-health/experts-and-stories/ask-acog/can-i-eat-sushi-while-im-pregnant

4. Centers for Disease Control and Prevention. About Listeria infection. https://www.cdc.gov/listeria/about/index.html

5. U.S. Food and Drug Administration. Listeria (Listeriosis). https://www.fda.gov/food/foodborne-pathogens/listeria-listeriosis

6. Interagency Food Safety Analytics Collaboration (CDC, FDA, USDA-FSIS). Foodborne illness source attribution estimates for Salmonella, E. coli O157, and Listeria monocytogenes — United States, 1998–2023. https://www.cdc.gov/ifsac/php/data-research/annual-report-2023.html

7. World Health Organization. Listeriosis fact sheet. https://www.who.int/news-room/fact-sheets/detail/listeriosis

8. Food Standards Agency. The risk to vulnerable consumers from Listeria monocytogenes in ready-to-eat smoked fish. 2024. https://www.food.gov.uk/research/foodborne-pathogens/the-risk-to-vulnerable-consumers-from-listeria-monocytogenes-in-ready-to-eat-smoked-fish

9. Lachmann R, et al. Invasive listeriosis outbreaks linked to salmon products, Germany, 2010–2021. [Full citation and DOI to be verified against PubMed before any manuscript use.]

10. Oster E. How dangerous is sushi during pregnancy, really? ParentData. https://parentdata.org/pregnancy/how-dangerous-sushi-during-pregnancy/