Birth Control in Clinical Trials: Industry Survey of Current Use Practices, Governance, and Monitoring
Bibliographic record
Abstract
The Health and Environmental Sciences Institute (HESI) Developmental and Reproductive Toxicology Technical Committee sponsored a pharmaceutical industry survey on current industry practices for contraception use during clinical trials. The objectives of the survey were to improve our understanding of the current industry practices for contraception requirements in clinical trials, the governance processes set up to promote consistency and/or compliance with contraception requirements, and the effectiveness of current contraception practices in preventing pregnancies during clinical trials. Opportunities for improvements in current practices were also considered. The survey results from 12 pharmaceutical companies identified significant variability among companies with regard to contraception practices and governance during clinical trials. This variability was due primarily to differences in definitions, areas of scientific uncertainty or misunderstanding, and differences in company approaches to enrollment in clinical trials. The survey also revealed that few companies collected data in a manner that would allow a retrospective understanding of the reasons for failure of birth control during clinical trials. In this article, suggestions are made for topics where regulatory guidance or scientific publications could facilitate best practice. These include provisions for a pragmatic definition of women of childbearing potential, guidance on how animal data can influence the requirements for male and female birth control, evidence-based guidance on birth control and pregnancy testing regimes suitable for low- and high-risk situations, plus practical methods to ascertain the risk of drug-drug interactions with hormonal contraceptives.
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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 | 0.180 | 0.282 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".