Teratogenic Risk Impact Mitigation (TRIM): Development of Explicit Criteria to Facilitate Decisions Regarding Teratogenic Risk Mitigation Strategies
Bibliographic record
Abstract
BACKGROUND: Preventing fetal exposure to teratogenic medications is an important target for risk mitigation efforts. Decisions about risk mitigation efforts specific to teratogenic medications are complex. OBJECTIVES: The Teratogenic Risk Impact and Mitigation (TRIM) tool was developed as an innovative decision support tool to facilitate prioritization of teratogenic medications for risk mitigation strategies. METHODS: We employed a modified Delphi study design involving experts across teratology, obstetrics/gynecology, and medication safety. Panelists proposed decision criteria in three focus groups, followed by e-Delphi rounds to reach a consensus on criteria regarding three dimensions: (1) completeness; (2) relevance; and (3) distinctiveness. Aggregated feedback from each round was used to inform revision of the criteria in subsequent rounds. RESULTS: A total of 33 candidate criteria proposed by 32 focus group participants were organized into ten distinct criteria for the Delphi process. Consensus (defined as > 85% agreement on all three dimensions) was reached after three e-Delphi rounds, resulting in six criteria: (1) background use among persons of reproductive potential; (2) overall medication benefit considering severity of the indication and availability of alternatives; (3) seriousness of the teratogenic outcome; (4) risk of the teratogenic outcome; (5) certainty regarding teratogenicity; and (6) the risk of exposure during pregnancy. CONCLUSIONS: We established measurable criteria to inform decisions when prioritizing teratogenic medications for risk mitigation programs. Criteria are consensus based and consistent with relevant regulatory guidance. Future work will operationalize these criteria and determine specific weights to facilitate medication-specific TRIM scores. Through its explicit framework, the TRIM tool may support consistent, transparent, and rational decision making and help optimize the contribution of risk mitigation programs to public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".