ABCDXXX: The obscenity of postmarketing surveillance for teratogenic effects
Bibliographic record
Abstract
Our current system of postmarketing surveillance, which is based on voluntary reporting of suspected teratogenic effects, is a failure. Postmarketing surveillance should, at a minimum, provide reassurance that every approved drug treatment does not produce a teratogenic effect as great as thalidomide embryopathy or fetal alcohol syndrome. This means that postmarketing surveillance should be able to detect a twofold or greater increase in the frequency of major congenital anomalies, a fivefold or greater increase in the frequency of intellectual disability, or a characteristic pattern of minor anomalies and functional abnormalities that occurs with a frequency of at least 10% among the children of women who were treated with the drug during pregnancy. Effective surveillance for teratogenic effects could be accomplished through a complementary set of mechanisms that includes pregnancy exposure registries or cohorts as well as direct examination of a small subset of infants whose mothers received the treatment during various periods of pregnancy. If this routine surveillance reveals a "signal" (i.e., an indication suggesting a possible teratogenic effect), further study would be needed to establish whether the observed effect is real and causal. Once a signal of possible teratogenicity in humans has been recognized, validating or refuting it would become an urgent matter.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.021 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".