A prospective comparison of cardiac rehabilitation enrollment following automatic vs usual referral
Bibliographic record
Abstract
OBJECTIVE: Cardiac rehabilitation remains grossly under-utilized despite its proven benefits. This study prospectively compared verified cardiac rehabilitation enrollment following automatic vs usual referral, postulating that automatic referral would result in significantly greater enrollment for cardiac rehabilitation. DESIGN: Prospective controlled multi-center study. PATIENTS AND METHODS: A consecutive sample of 661 patients with acute coronary syndrome treated at 2 acute care centers (75% response rate) were recruited, one site with automatic referral via a computerized prompt and the other with a usual referral strategy at the physician's discretion. Cardiac rehabilitation referral was discerned in a mailed survey 9 months later (n = 506; 84% retention), and verified with 24 cardiac rehabilitation sites to which participants were referred. RESULTS: A total of 124 (52%) participants enrolled in cardiac rehabilitation following automatic referral, vs 84 (32%) following usual referral (p < 0.001). Automatically referred participants were more likely to be referred from an in- patient unit (p < 0.01), and to be referred in a shorter time period (p < 0.001). Logistic regression analyses revealed that, after controlling for sociodemographic characteristics and case-mix, automatically referred participants were significantly more likely to enroll in cardiac rehabilitation (odds ratio = 2.1; 95% confidence interval 1.4-3.3) than controls. CONCLUSION: Automatic referral resulted in over 50% verified cardiac rehabilitation enrollment; 2 times more than usual referral. It also significantly reduced utilization delays to less than one month.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".