Bibliographic record
Abstract
OBJECTIVE: To determine how long it takes after a new drug is marketed to establish whether or not its use by pregnant women is likely to pose a substantial teratogenic risk. METHODS: We used standard clinical teratology resources to assess the teratogenic risks in human pregnancy of therapeutic treatment with 468 drugs approved by the US Food and Drug Administration between 1980 and 2000. The teratogenic risk of each treatment was classified using the current online version of TERIS into one of three categories: 1) no risk, minimal risk, or unlikely to produce an increased risk; 2) associated with a small, moderate, or high risk; or 3) risk undetermined. RESULTS: We found that the teratogenic risk in human pregnancy was still undetermined for 91.2% of drug treatments approved in the United States between 1980 and 2000. The proportion of treatments classified as having an "undetermined" teratogenic risk was more than 80% for drugs approved for marketing 0-4, 5-9, 10-14, or 15-20 years ago, but the highest proportion of drugs with an "undetermined" teratogenic risk was found among those approved 15-20 years ago. The agreement between TERIS risk ratings and Food and Drug Administration Use-in-Pregnancy Categories for 163 drugs that had been assessed by both systems was poor (kappa +/- standard error = 0.082 +/- 0.042). CONCLUSION: We conclude that inadequate information is available for pregnant women and their physicians to determine whether the benefits exceed the teratogenic risks for most drug treatments introduced in the past 20 years.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".