Postmarketing surveillance for human teratogenicity: A model approach
Bibliographic record
Abstract
BACKGROUND: Most congenital defects associated with prenatal exposures are notable for a pattern of major and minor malformations, rather than for a single major malformation. Thus, traditional epidemiological methods are not universally effective in identifying new teratogens. The purpose of this report is to outline a complementary approach that can be used in addition to other more established methods to provide the most comprehensive evaluation of prenatal exposures with respect to teratogenicity. METHODS: We describe a multicenter prospective cohort study design involving dysmorphological assessment of liveborn infants. This design uses the Organization of Teratology Information Services, a North American network of information providers who also collaborate for research purposes. Procedures for subject selection, methods for data collection, standard criteria for outcome classification, and the approach to analysis are detailed. RESULTS: The focused cohort study design allows for evaluation of a spectrum of adverse pregnancy outcomes ranging from spontaneous abortion to functional deficit. While sample sizes are typically inadequate to identify increased risks for single major malformations, the use of dysmorphological examinations to classify structural anomalies provides the unique advantage of screening for a pattern of malformation among exposed infants. CONCLUSIONS: As the known human teratogens are generally associated with patterns of structural defects, it is only when studies of this type are used in combination with more traditional methods that we can achieve an acceptable level of confidence regarding the risk or safety of specific exposures during pregnancy.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".