Update on new developments in the study of human teratogens
Bibliographic record
Abstract
BACKGROUND AND METHODS: The purpose of this annual article is to highlight and briefly review new and significant information on agents that may be teratogenic in pregnant women. Various sources of on-line and printed information are given. RESULTS: The following topics have been discussed: 1) lithium medication: decreased estimate of risk; 2) cigarette smoking and genotype as contributors to oral-facial clefts and clubfoot; 3) trimethoprim; 4) methimazole syndrome?; 5) glucocorticoids and oral-facial clefts; 6) binge drinking; 7) fetal valproate syndrome; and 8) carbamazepine. CONCLUSIONS: We have highlighted several maternal exposures during pregnancy that are associated with small but increased rates of birth defects, generally only a few cases per 1,000 infants. These exposures include cigarette smoking, and treatment with lithium, trimethoprim, methimazole, or corticosteroids. This weak teratogenic effect was usually identified by the linkage of an uncommon treatment with an unusual birth defect outcome. The use of modern epidemiologic techniques, especially prospective multicenter studies that provide increased numbers, has helped to strengthen the evidence for these associations. We discuss how teratogenic risks that are small in comparison to the background risk can be presented to at-risk women and their doctors. We have briefly listed some elements that might be used in prioritizing further studies of suspected teratogenic exposures. Various existing methods for expressing the strength of evidence for human teratogenicity are also given.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".