Predicting cardiac rehabilitation enrollment: the role of automatic physician referral
Bibliographic record
Abstract
BACKGROUND: Despite the established benefits of cardiac rehabilitation, evidence suggests referral to, and subsequent enrollment in, cardiac rehabilitation following a coronary event remains low (10-25%). The aim of this study was to identify predictors of attendance to cardiac rehabilitation intake and subsequent enrollment in rehabilitation after coronary artery bypass graft surgery within the framework of an automatic referral system. DESIGN AND METHODS: We conducted a historic prospective study of patients who underwent coronary artery bypass graft surgery between 1 April 1996 and 31 March 2000 and lived within the geographic referral area of a multi-disciplinary cardiac rehabilitation center in central-south Ontario, Canada. Coronary artery bypass graft surgery patients are automatically referred to cardiac rehabilitation at the time of hospital discharge. Consecutive health records of eligible patients were reviewed for medical history, cardiac risk factor profiles, and evidence of cardiac rehabilitation intake attendance and enrolment. RESULTS: A total of 3536 patients met eligibility criteria. Patients were predominantly male (79.1%), approximately 64 years of age, living with a spouse or a partner, English-speaking, retired and had multiple cardiac risk factors. Of eligible patients, 2121 (60.0%) attended the cardiac rehabilitation intake appointment. Of patients who attended cardiac rehabilitation intake 1463 (69%) enrolled in at least one cardiac rehabilitation service, based on their risk factor profile. Selected cardiac rehabilitation services were exercise training (n=1287; 88%), nutrition counseling (n=571; 39.0%), nursing care (n=546; 37.3%), and psychological intervention (n=223; 15.2%). CONCLUSIONS: An institutionalized, physician-endorsed system of automatic referral to cardiac rehabilitation resulted in higher rates of cardiac rehabilitation intake and enrollment following coronary artery bypass graft surgery than previously reported and should be adopted for all cardiac populations.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".