Vaccine regulation should require and enforce the inclusion of pregnant and breastfeeding women in prelicensure clinical trials
Bibliographic record
Abstract
Exclusion of pregnant and breastfeeding women from the pivotal randomized controlled trials for COVID-19 vaccines that led to emergency regulatory approval created gaps in data needed for vaccine policy, healthcare provider recommendations, and women's decisions about vaccination. We argue that such knowledge gaps increase potential for vaccine hesitancy and misinformation relating to the health of women and infants, and that these gaps in evidence are avoidable. Over several decades, ethical and scientific guidance, scholarship, and advocacy in favor of pregnant and breastfeeding women's participation in clinical development of vaccines has accumulated. Guidance on how to include pregnant and breastfeeding women in vaccine trials ethically and safely predates the COVID-19 pandemic but has yet to be routinely incorporated in vaccine development. We highlight the important role regulatory authorities could play in requiring that pregnant and breastfeeding women be eligible as volunteer participants in prelicensure vaccine trials for products that are expected to be used in this population. Inclusion of pregnant and breastfeeding populations in clinical trials leading to market approval or emergency use authorization should be undertaken early or concurrently at the time of trials in the general population.
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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 | 0.602 | 0.624 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.052 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".