Could Estrogen Protect Women From COVID-19?
Bibliographic record
Abstract
The apparent gender differences in favor of women in the risk of contracting and dying from coronavirus disease 2019 (COVID-19), and the fact that such trends have also been observed in recent epidemics including severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), have prompted the obvious question: Are the reasons life-style or biological? True, women generally make healthier lifestyle choices as compared to men. Women do not smoke or drink as much as men, and they have a lower burden of those diseases (heart disease, diabetes or chronic lung conditions) that are known to be significant factors in the higher death rates among men with COVID-19. But there is compelling evidence for a role for biological factors. Genes are likely to play an important role. The X chromosome, of which women possess two, contains the largest number of immune-related genes of the whole human genome, theoretically giving women double the advantage over men in mounting an efficient and rapid immune response. A fundamental difference between women and men is their hormonal milieu, and it is not unreasonable to suppose that the dominant female hormone estrogen could influence the response to infection. In this paper we evaluate the evidence and mechanisms by which estrogen could provide protection to women from a variety of viruses, perhaps including the coronavirus that causes 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".