Risk for Re-Enrollment to Cardiac Rehabilitation: A Retrospective Study of Ontario-Based Cardiac Rehabilitation Programs
Bibliographic record
Abstract
Cardiac rehabilitation (CR) reduces recurrent cardiac events, and cardiovascular disease-related mortality, and increases overall quality of life among individuals with heart disease. Some participants have recurrent cardiac events and require re-referral to CR; however, it is not known whether the risk for recurrent events can be predicted and possibly mitigated. Thus, the purpose of this study was to describe CR re-referral and subsequent re-enrollment rates and understand the impact of risk factors on the risk of re-enrollment. In this study, data from individuals who were referred to CR (n = 1602) in 2008 and individuals who enrolled (n = 930) at two Southwestern Ontario CR programs over a five-year period were used. CR re-referral was defined as a second event within five years of initial discharge in 2008 that may or may not have resulted in re-enrollment, whereas re-enrollment was defined as a second admission to CR. Time to re-referral and re-enrollment by hospital site was described using the Kaplan Meier method and log-rank test. Of the 930 participants who enrolled (58% of individuals referred to CR), 27 (2.9%) participated in CR a second time. The time of re-enrollment was not influenced significantly by program site (Mean (M)=4.89 years, 95% CI 4.84 - 4.95, p =.75 and M=4.92 years, 95% CI 4.87 - 4.97). Findings suggest that individuals who participate in CR the first time have a low rate of re-enrollment and that Ontario CR programs have a consistent model of care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".