Exclusion of pregnant and lactating persons from breast cancer clinical trials: a review of active trials registered on ClinicalTrials.gov
Bibliographic record
Abstract
INTRODUCTION: Treatment of pregnancy-associated breast cancer is complex, as providers try to balance risks to the pregnant person and the developing fetus. Given increased case fatality and increasing incidence, there is a pressing need understand the efficacy and safety of different treatment regimens in this population; however, pregnant and lactating people have traditionally been excluded from participating in randomized controlled trials (RCTs). Given recent efforts to expand the inclusion criteria for oncology RCTs, this study aimed to review the inclusion/exclusion criteria of current breast cancer RCTs to assess what proportion of trials permitted enrollment of pregnant and lactating persons. MATERIAL AND METHODS: We conducted a comprehensive search of ClinicalTrials.gov in January 2022 to identify interventional studies of breast cancer in adults that were actively recruiting. The primary outcomes were the exclusion of pregnant and lactating people. RESULTS: The search identified 1706 studies, of which 1451 met eligibility criteria. Overall, 69.4% and 54.8% of studies excluded pregnant and lactating people, respectively. The exclusion of pregnant and lactating persons differed by study characteristics but extended across all trial designs, locations, phases and interventions. Exclusion of pregnant and lactating persons was most common in trials where the intervention was biological (86.3%), drug (83.5%) or radiation (81.5%). CONCLUSIONS: The exclusion of pregnant and lactating people from clinical trials contributes to evidence gaps in how to treat this population. A paradigm shift is needed that focuses on how research can be used to protect pregnant people from future harms, instead of protecting pregnant people from research-related risks.
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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 | 0.080 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".