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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".