Does gender influence clinical expression and disease outcomes in COVID-19? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) was characterized at the end of 2019, and soon spread around the world, generating a pandemic. It has been suggested that men are more severely affected by the viral disease (COVID-19) than women. OBJECTIVE: The aim of this systematic literature review (SRL) and meta-analysis was to analyse the influence of gender on COVID-19 mortality, severity, and disease outcomes. A SRL was performed in PubMed and Embase, searching terms corresponding to the 'PEO' format: population = adult patients affected with COVID-19; exposure = gender; outcome = any available clinical outcomes by gender, including mortality and disease severity. The search covered the period from January 1 to April 30, 2020. Exclusion criteria were: case reports/series, reviews, commentaries, languages other than English. Full-text, original articles were included. Data on study type, country, and patients' characteristics were extracted. Study quality was evaluated using the Newcastle-Ottawa scale (NOS). From a total of 950 hits generated by the database search, 85 articles fulfilling the inclusion criteria were selected. RESULTS: A random-effects meta-analysis was performed to compare mortality, recovery rates, and disease severity in men compared with women. The male to female ratio for cases was 1:0.9. A significant association was found between male sex and mortality (OR = 1.81; 95% CI 1.25-2.62), as well as a lower chance of recovery in men (OR = 0.72; 95% CI 0.55-0.95). Male patients were more likely to present with a severe form of COVID-19 (OR = 1.46; 95% CI 1.10-1.94). CONCLUSIONS: Males are slightly more susceptible to SARS-CoV2 infection, present with a more severe disease, and have a worse prognosis. Further studies are warranted to unravel the biological mechanisms underlying these observations.
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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 | 0.002 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".