Pregnant Women in Trials of Covid‐19: A Critical Time to Consider Ethical Frameworks of Inclusion in Clinical Trials
Bibliographic record
Abstract
Ethical issues abound during this unprecedented international public health crisis of Covid-19. While the trade-off between societal and individual interests that occurs at the intersection of public health ethics and clinical ethics affects all populations, this calculus has particular relevance for pregnant women and the question of when they will have access to new Covid-19 therapies and vaccines. Pregnant women are a "scientifically complex" population whose inclusion in clinical research must be done with consideration of the unique state of pregnancy. Yet research on the impact of Covid-19 on pregnant women is lagging. In a rush to prevent and treat SARS-CoV-2 infection, it is crucial that the interests of pregnant women be prioritized to enable them to make autonomous, informed decisions about participating in clinical trials. The global pandemic calls for a revisiting of frameworks for the inclusion of pregnant women in research, as these women have an important stake in the prevention and treatment of Covid-19.
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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 | 0.753 | 0.772 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.065 |
| Scholarly communication | 0.050 | 0.052 |
| Open science | 0.014 | 0.024 |
| Research integrity | 0.064 | 0.101 |
| 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".