P76 The effect of the pregnancy and lactation labeling rule on prescribing information of FDA-approved drugs
Bibliographic record
Abstract
Background The U.S. Food and Drug Administration implemented the new Pregnancy and Lactation Labeling Rule (PLLR) in June 2015. Under PLLR, all new drug applications were to present a narrative risk assessment (as opposed to letter category), while drug approvals after June 2001, were required to phase in by June 2020. The purpose of this study was to assess the quality of presented pregnancy and lactation data in the drug labeling and degree of adherence to the PLLR. Design/Methods We reviewed the labeling data of all new molecular entities (NMEs) approved from 1999–2017. The pregnancy and lactation information was classified as: 1. Harmful to use 2. Safe to use 3. Consideration of safety and efficacy. For drugs approvals after June 2001, presence of pregnancy letter category system was noted. Results Of the 456 NMEs, 131 (29%) were classified as harmful to use in pregnancy and 207 (45%) as harmful to use during lactation. This number did not follow any specific pattern over the course of 19 years. Less than 1% of drugs were deemed to be safe during pregnancy or lactation. Human data was the source of pregnancy or lactation information for only 2% of drugs. Up to 70% of drugs belonged to each implementation schedule has yet to meet the PLLR compliance requirement. Conclusion(s) Pregnant and lactating women are mostly advised against use of medications that might be needed for their health and health of their infants based on very limited data. Pharmaceutical companies lagged behind the required adherence rule for labeling updates on pregnancy and lactation information. Disclosure(s) Nothing to disclose
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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 | 0.139 | 0.483 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".