Clinical and sociodemographic correlates of referral for cardiac rehabilitation following cardiac revascularization in Ontario
Bibliographic record
Abstract
OBJECTIVES: Describe rates of, and examine factors affecting, referral to cardiac rehabilitation (CR) following revascularization in Ontario. BACKGROUND: CR reduces mortality following cardiac revascularization, but is largely underutilized, partly due to poor referral rates. METHODS: In this retrospective study, the sample consisted of all CR-indicated patients who underwent revascularization at the Cardiac Care Network of Ontario hospitals between October 2011 through March 2012. Referral rates were described, and multivariate analyses performed to identify disparities. RESULTS: Of the 3739 patients included, 51.8% were referred to CR. Patients aged ≥85 or requiring a translator, and patients with hyperlipidemia, heart failure, or comorbid pulmonary, renal or peripheral vascular disease, were significantly less likely to be referred. Patients with a history of smoking or myocardial infarction, or who underwent coronary artery bypass graft surgery, were significantly more likely. CONCLUSIONS: A national policy statement recommends 85% referral of indicated patients to CR, a target currently missed by almost 35%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".