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].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".