Sex-Based Differences in Severe Outcomes, Including Cardiovascular Hospitalization, in Adults With COVID-19 in Ontario, Canada
Bibliographic record
Abstract
Background: While men have experienced higher risks of SARS-CoV-2 infection compared to women, an analysis of sex differences by age in severe outcomes during the acute phase of infection is lacking. Objectives: The purpose of this study was to assess heterogeneity in severe outcome risks by age and sex by conducting a retrospective cohort study of community-dwelling adults in Ontario who tested positive for SARS-CoV-2 infection during the first 3 waves. Methods: Adjusted odds ratios were estimated using multilevel multivariable logistic regression models including an interaction term for age and sex. The primary outcome was a composite of severe outcomes (hospitalization for a cardiovascular (CV) event, intensive care unit admission, mechanical ventilation, or death) within 30 days. Results: for interaction <0.05). Men with SARS-CoV-2 infection experienced a higher risk of outcomes than infected women of the same age, except for the risk of all-cause hospitalization being higher for young women than men (ages 18-45 years) during waves 2 and 3. The sex disparity in CV hospitalization across all ages either persisted or increased with each subsequent wave. Conclusions: To mitigate risks in subsequent waves, it is helpful to further understand the factors that contribute to the generally higher risks faced by men across all ages, and the persistent or increasing sex disparity in the risk of CV hospitalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".