Cardiac rehabilitation referral, attendance and mortality in women
Bibliographic record
Abstract
BACKGROUND: Cardiac rehabilitation (CR) reduces mortality in women and men with coronary artery disease (CAD). The objective of this study was to examine sex differences in long-term mortality, based on CR referral rates and attendance patterns in a large CAD population. DESIGN: This is a retrospective cohort study. METHODS: The Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) and Cardiac Wellness Institute of Calgary (CWIC) databases were used to obtain information on all patients. Rates of referral to and attendance at CR were compared by sex. Logistic regression models were constructed to assess whether sex predicted CR referral or completion. The association between referral, completion, and survival was assessed by sex using Cox proportional hazard models. RESULTS: 25,958 subjects (6374-24.6%-were women) with at least one vessel CAD were included. Females experienced reduced rates of CR referral (31.1% vs 42.2%, p < 0.0001) and completion (50.1 vs 60.4%, p < 0.0001). Adjusting for demographic and clinical characteristics, relative to men, CR referral was significantly lower in women (adjusted odds ratio (OR) 0.74, 95% CI 0.69, 0.79) as was CR completion (adjusted OR 0.73, 95% CI 0.66, 0.81). Women completing CR experienced the greatest reduction in mortality (HR 0.36, 95% CI 0.28, 0.45) with a relative benefit greater than men (HR 0.51, 95% CI 0.46, 0.56). CONCLUSION: This is the first large cohort study to demonstrate that referral to and attendance at CR is associated with a significant mortality reduction in women, comparatively better than that in men.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".