The COVID-19 vaccine evidence gap: Decisions without data for pregnant and breastfeeding women
Bibliographic record
Abstract
On March 11th, 2020, the Word Health Organization officially declared the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak a pandemic, setting in motion an unprecedented modern-day global public health response [1, 2]. Since that time, COVID-19’s impact has been wide-reaching and complex; influenced by a diverse set of biologic, clinical, psychologic, and sociodemographic factors [3, 4]. Indeed, not all members of society are at equal risk of SARS-CoV-2 infection or experiencing severe infection-related outcomes [3, 4]. For example, older adults and individuals with comorbid conditions (e.g., cardiovascular disease) are at increased risk for severe disease [5]. Further, males are at higher risk of serious COVID-19 outcomes compared to females, underscoring the importance of including sex as a fundamental variable in the design and conduct of COVID-19 research [6]. Certainly, without sex-disaggregated data, it is impossible to know if, or to what extent, sex-specific approaches to the care and prevention of SARS-CoV-2 infection should be employed [7].
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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 | 0.197 | 0.570 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 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".