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 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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".