AVOIDING GIGO MEDICAL RESEARCH

A COUPLE DAYS AGO, “ARTIFICIAL INTELLIGENCE AND LOOPHOLES,” SimanaitisSays, July 12, 2026, offered tidbits gleaned from Celina Zhoa’s AAAS Science article, “A.I. Models Have a Troubling Knack For Discovering Legal Loopholes.”

Wouldn’t you know, the next issue of Science has Frederick Joelving, Retraction Watch, reporting similar problems within medical research: “Medical Students Are Using a Popular Research Tool To Pump Out Misleading Studies,” Science, June 25, 2026. 

Is TriNetX Encouraging “Garbage In/Garbage Out”? Frederick Joelving observes, “Critics say TriNetX’s easy analyses of electronic medical records fuel quick-and-dirty publications from inexperienced authors.” He quotes researcher Joshua Wang, a neuroscientist at Taipei Tzu Chi Hospital who trains scientists there in using TriNetX: Some result, Wang says, look “a bit dodgy.”

Joelving recounts, “He and others say the easy-to-use platform may be allowing inexperienced researchers—potentially aided by artificial intelligence (A.I.)—to churn out unreliable and bias-ridden studies with unrivaled speed. ‘We’ve seen a lot of these TriNetX studies, and they all seem to have very similar flaws,’ says Samy Suissa, a pharmacoepidemiologist at McGill University. ‘They seem to always find these spectacular effects, remarkable benefits for drugs on all kinds of outcomes.’ ”

The Growth of TriNetX. Joelving cites, “In 2025, nearly 2700 publications mentioned TriNetX in the title or abstract, up from just 33 only 5 years prior, according to the Dimensions database, which tracks abstracts and citations. Less than halfway through this year, the number already exceeds 2100.” 

Joelving continues, “… most TriNetX papers come from authors at U.S. medical schools, often with a physician-in-training as lead author. Medical schools use TriNetX as a research training ground, and the resulting papers are a relatively easy way for medical students to boost their CVs before applying for residencies. ‘There is no substitute for learning this process than by doing it,’ says Lisa Howley of the Association of American Medical Colleges (AAMC).”

Lack of Correcting for Biases. Joelving recounts, “But the combination of inexperienced users and TriNetX’s push-button analysis tools can lead to shoddy publications, which often do not correct for potential biases that can make treatments appear more effective than they are. And because the data can be analyzed so quickly, users can easily cherry-pick positive results for publication, a practice known as p-hacking. ‘The flow of false discoveries is hugely greater,’ says Matt Spick, a health-data scientist at the University of Surrey.”

An Example: Cancers in the Obese. Joelving shares an example from Wang and other researchers: “They point to a TriNetX paper published in the MDPI journal Cancers that made the news for finding what the authors described as ‘compelling evidence’ that popular GLP-1 weight-loss drugs lower the risk for a long list of cancers in obese people. The paper failed to mention, let alone correct for, two key biases that can skew results in favor of the treatment being studied, called collider bias and immortal-time bias.”

These Two Key Oversights: “Collider bias,” Joelving explains, “can arise when both an exposure—for example, to a weight-loss drug—and an outcome such as cancer drive health care use, the so-called collider. The bias can create a spurious negative correlation between the exposure and outcome.”

There’s always the challenge of separating correlation versus causality. See “a community’s alcohol sales and teachers’ pay” at “Four Things Logicians Don’t Want You To Know,” SimanaitisSays, July 16, 2016.

“Immortal-time bias,” Joelving continues, “can occur when researchers compare outcomes between patients who receive a certain treatment after a health event—say, a heart attack—and those who do not get the treatment, because any patient who dies before treatment automatically becomes part of the untreated group. That group then appears to have higher mortality.” 

Well, duh.

Fudging With TriNetX. Joelving recounts, “In other cases, papers claim to have used TriNetX to do analyses the platform doesn’t in fact offer. Wang came across a paper published in Angiology suggesting diabetes drugs call gliflozins could reduce the risk of death after a heart attack. The authors, physicians at three top-tier U.S. medical schools, wrote they had conducted a key step to correct for immortal-time bias within TriNetX. Wang knew TriNetX offers no such tool.”

Promoting Best Practices: From Quantity to Quality. Joelving concludes, “Meanwhile, Wang continues his daily vigil, and he is working to promote best practices. At his own hospital, researchers seeking access to TriNetX must first complete a 1-hour training session with him. A lot of that time, he says, is spent illustrating how easy it is to get ‘beautiful-looking’ but meaningless results. The hope, he says, is to ‘try and instill a little bit of fear so that they don’t run off and churn it out.’ ”

That is, avoid GIGO. ds

© Dennis Simanaitis, SimanaitisSays.com, 2026