Sex differences in clinical phenotype and transitions of care among individuals dying of COVID-19 in Italy
Bibliographic record
Abstract
BACKGROUND: Among the unknowns posed by the coronavirus disease 2019 (COVID-19) outbreak, the role of biological sex to explain disease susceptibility and progression is still a matter of debate, with limited sex-disaggregated data available. METHODS: A retrospective analysis was performed to assess if sex differences exist in the clinical manifestations and transitions of care among hospitalized individuals dying with laboratory-confirmed SARS-CoV-2 infection in Italy (February 27-June 11, 2020). Clinical characteristics and the times from symptoms' onset to admission, nasopharyngeal swab, and death were compared between sexes. Adjusted multivariate analysis was performed to identify the clinical features associated with male sex. RESULTS: Of the 32,938 COVID-19-related deaths that occurred in Italy, 3517 hospitalized and deceased individuals with COVID-19 (mean 78 ± 12 years, 33% women) were analyzed. At admission, men had a higher prevalence of ischemic heart disease (adj-OR = 1.76, 95% CI 1.39-2.23), chronic obstructive pulmonary disease (adj-OR = 1.7, 95% CI 1.29-2.27), and chronic kidney disease (adj-OR = 1.48, 95% CI 1.13-1.96), while women were older and more likely to have dementia (adj-OR = 0.73, 95% CI 0.55-0.95) and autoimmune diseases (adj-OR = 0.40, 95% CI 0.25-0.63), yet both sexes had a high level of multimorbidity. The times from symptoms' onset to admission and nasopharyngeal swab were slightly longer in men despite a typical acute respiratory illness with more frequent fever at the onset. Men received more often experimental therapy (adj-OR = 2.89, 95% CI 1.45-5.74) and experienced more likely acute kidney injury (adj-OR = 1.47, 95% CI 1.13-1.90). CONCLUSIONS: Men and women dying with COVID-19 had different clinical manifestations and transitions of care. Identifying sex-specific features in individuals with COVID-19 and fatal outcome might inform preventive strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".