Physician Perspectives on Including Pregnant Women in Covid‐19 Clinical Trials: Time for a Paradigm Change
Bibliographic record
Abstract
Excluding pregnant people from Covid-19 clinical trials may lead to unintended harmful consequences. For this study, an online questionnaire was sent to physicians belonging to Canadian professional medical associations in order to evaluate their perspectives on the participation of pregnant women in Covid-19 clinical trials. The majority of respondents expressed support for including pregnant women in Covid-19 trials (119/165; 72%), especially those investigating therapies with a prior safety record in pregnancy (139/164; 85%). The main perceived barriers to inclusion identified were unwillingness of pregnant patients to participate and of treating teams to offer participation, the burden of regulatory approval, and a general "culture of exclusion" of pregnant women from trials. We describe why some physicians may be reluctant to include pregnant individuals in trials, and we identify barriers to the appropriate participation of pregnant people in clinical research.
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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 | 0.077 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".