Systematic procedure for the classification of proven and potential teratogens for use in research
Bibliographic record
Abstract
BACKGROUND: Although there is strong evidence that some medications are teratogenic, the current lists of teratogens to be used in research are outdated. The objective of this study was to develop an updatable and systematic procedure to the classification of medications proven and potentially teratogenic in the first trimester of pregnancy, for use in research. METHODS: We developed a two-step procedure for teratogen classification. Step 1 includes classifying the medications from Drugs in Pregnancy and Lactation: a Reference Guide to Fetal and Neonatal Risk (9th ed.) into two provisional lists: (1) teratogenic medications, and (2) potentially teratogenic medications. We also searched other references to add other medications. In Step 2, the Teratology Information System (TERIS) database was searched, and the medication was classified as teratogenic or potentially teratogenic according to a newly developed scheme. Expert consensus was used if a medication was not recorded in TERIS. RESULTS: A total of 114 medications were identified in Drugs in Pregnancy and Lactation: a Reference Guide to Fetal and Neonatal Risk, with 57 medications in each provisional list. Seventy-eight medications were identified in other sources. A total of 135 medications were included in Step 2; the TERIS scheme classified 23 medications, and 112 medications required expert opinion. The two experts agreed on 78.6% of the medications (kappa = 0.63). We identified 91 teratogenic and 81 potentially teratogenic medications. CONCLUSION: Using reliable references, we established a systematic procedure to the classification of medications with evidence of or potential teratogenic risk. These exhaustive lists will be useful in teratology research and related fields.
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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 | 0.306 | 0.484 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.059 | 0.026 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".