Effectiveness of inpatient and outpatient strategies in increasing referral and utilization of cardiac rehabilitation: a prospective, multi-site study
Bibliographic record
Abstract
BACKGROUND: Despite the evidence of benefit, cardiac rehabilitation (CR) remains highly underutilized. The present study examined the effect of two inpatient and one outpatient strategy on CR utilization: allied healthcare provider completion of referral (a policy that had been endorsed and approved by the cardiac program leadership in advance; PRE-APPROVED); CR intake appointment booked before hospital discharge (PRE-BOOKED); and early outpatient education provided at the CR program shortly after inpatient discharge (EARLY ED).In this prospective observational study, 2,635 stable cardiac inpatients from 11 Ontario hospitals completed a sociodemographic survey, and clinical data were extracted from charts. One year later, participants were a mailed survey that assessed CR use. Participating inpatient units and CR programs to which patients were referred were coded to reflect whether each of the strategies was used (yes/no). The effect of each strategy on participants' CR referral and enrollment was examined using generalized estimating equations. RESULTS: A total of 1,809 participants completed the post-test survey. Adjusted analyses revealed that the implementation of one of the inpatient strategies was significantly related to greater referral and enrollment (PRE-APPROVED: OR = 1.96, 95%CI = 1.26 to 3.05, and OR = 2.91, 95%CI = 2.20 to 3.85, respectively). EARLY ED also resulted in significantly greater enrollment (OR = 4.85, 95%CI = 2.96 to 7.95). CONCLUSIONS: These readily-implementable strategies could significantly increase access to and enrollment in CR for the cardiac population. The impact of these strategies on wait times warrants exploration.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".