Perpetuating fears: bias against the null hypothesis in fetal safety of drugs as expressed in scientific citations.
Bibliographic record
Abstract
BACKGROUND: Bias against negative studies (i.e., those showing no issues with fetal safety of drugs) may cause distorted interpretation with apparently safe drugs being labeled as teratogenic, causing women to terminate pregnancy or not to treat serious medical conditions. OBJECTIVE: To investigate whether "positive" studies, claiming teratogenic effects of drugs, which were later shown to be safe, have been cited more often than "negative" studies on the same topic. METHODS: We reviewed published studies on the fetal safety of 6 drugs, which were the focus of appreciable controversy over the last 5 decades (oral contraceptives, bendectin®, benzodiazepines, paroxetine, ACE inhibitors and statins). While initial highly publicized papers claimed teratogenic effects, these were subsequently contradicted by large numbers of "negative" studies. We compared medical citation patterns of the "positive" vs. "negative" papers related to these 6 drugs.Results"Positive" papers were 70% more likely to be cited than "negative" articles (median 39 vs. 23, p=0.04). In multivariate linear regression, "positivity" of results (p=0.04), the number of years since publication (p=0.01) and journal citation impact (p<0.001) all independently predicted the total number of medical citations. CONCLUSIONS: We documented bias against the null hypothesis in medical citations of fetal drug safety. Acknowledging this source of bias is critical in trying to avert the distortion of the medical knowledge created by it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.300 | 0.715 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".