Examining mechanisms for gender differences in admission to intensive care units
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether the male predominance of older people admitted to intensive care units (ICUs) is due to gender differences in the presence of spouses, partners, or children; rates of gender-specific disease; or triage decisions made by health system personnel. DATA SOURCES AND COLLECTION: Three population-based datasets, 2004-2012, of Canadians ≥65 years: provincial health care data from Manitoba (n = 250 190) and national data of nursing home residents (n = 133 982) and community-based homecare recipients (n = 210 090). STUDY DESIGN: Retrospective observational study, using multivariable Cox proportional hazards and logistic regression. PRINCIPAL FINDINGS: Males predominated in ICU admissions: from Manitoba (hazard ratio [HR] = 1.87, 95% CI = 1.80-1.95), nursing homes (HR = 1.47, 1.35-1.60), and homecare (odds ratio = 1.14, 1.11-1.17). Adjustment for spouses, partners, and children did not attenuate this effect. The HR for gender was lower by 13.5 percent, relative, after excluding ICU care for cardiac causes. Male predominance was not present during a second ICU admission among survivors of a first ICU-containing hospitalization (HR = 1.07, 0.96-1.20). CONCLUSIONS: In three older cohorts, the male predominance of ICU admission was not explained by gender differences in the presence of a spouse, partner, or children, or cardiac disease rates. The third finding suggests that triage bias is unlikely to be responsible for the male predominance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".