The sex and gender dimensions of COVID-19: A narrative review of the potential underlying factors
Why this work is in the frame
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Bibliographic record
Abstract
Multiple lines of evidence indicate that the male sex is a significant risk factor for severe disease and mortality due to coronavirus disease 2019 (COVID-19). However, the precise explanation for the discrepancy is currently unclear. Immunologically, the female-biased protection against COVID-19 could presumably be due to a more rapid and robust immune response to viruses exhibited by males. The female hormones, e.g., estrogens and progesterone, may have protective roles against viral infections. In contrast, male hormones, e.g., testosterone, can act oppositely. Besides, the expression of the ACE-2 receptor in the lung and airway lining, which the SARS-CoV-2 uses to enter cells, is more pronounced in males. Estrogen potentially plays a role in downregulating the expression of ACE-2, which could be a plausible biological explanation for the reduced severity of COVID-19 in females. Comorbidities, e.g., cardiovascular diseases, diabetes, and kidney disorders, are considered significant risk factors for severe outcomes in COVID-19. Age-adjusted data shows that males are statistically more predisposed to these morbidities-amplifying risks for males with COVID-19. In addition, many sociocultural factors and gender-constructed behavior of men and women impact exposure to infections and outcomes. In many parts of the world, women are more likely to abide by health regulations, e.g., mask-wearing and handwashing, than men. In contrast, men, in general, are more involved with high-risk behaviors, e.g., smoking and alcohol consumption, and high-risk jobs that require admixing with people, which increases their risk of exposure to the infection. Overall, males and females suffer differently from COVID-19 due to a complex interplay between many biological and sociocultural factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it