Bias Toward the Null Hypothesis in Pregnancy Drug Studies That Do Not Include Data on Medical Terminations of Pregnancy: The Folic Acid Antagonists
Bibliographic record
Abstract
Most studies on safety/risk of drugs in pregnancy consider the proportion of births (but not pregnancy terminations) affected by the drug from all exposed infants. Lack of data on pregnancy terminations could bias results. A computerized database for medications dispensed to pregnant women in southern Israel was linked with records from the district hospital; 84 823 deliveries and 998 medical pregnancy terminations took place; 571 of the women were exposed to folic acid antagonists in the first trimester. When only births were examined, there was no association between folic acid antagonists and fetal malformations. When data on pregnancy terminations were examined and births and pregnancy terminations were combined, there was a significant risk (neural tube defects: odds ratio 18.83, 95% confidence interval 9.24-38.37; cardiovascular defects: odds ratio 3.86, 95% confidence interval 1.67-8.88; and neural tube defects: odds ratio 6.30, 95% confidence interval 3.34-9.15; cardiovascular defects: odds ratio 1.76, 95% confidence interval 1.05-2.92, respectively). Inclusion of only birth data in observational studies of drugs in pregnancy constitutes a source of bias toward the null hypothesis.
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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 | 0.451 | 0.659 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".