Abstract 134: Implementation of Home-Based Cardiac Rehabilitation in the VA
Bibliographic record
Abstract
Objective: Referral to cardiac rehabilitation (CR) is one of nine performance measures for patients with ischemic heart disease (IHD), but fewer than 20% of eligible patients participate in the United States. Home-based CR programs (available in the United Kingdom, Australia, and Canada) have similar effects on morbidity and mortality as traditional (facility-based) CR, but they are not currently available or reimbursed in the US. We sought to determine whether implementing home-based programs could increase CR participation among patients with IHD. Methods: Using electronic health records from 134 VA medical centers, we identified 106,277 veterans hospitalized for acute myocardial infarction, percutaneous coronary intervention or coronary artery bypass grafting between 2010 and 2015. We compared the proportion of eligible patients who participated in CR at 13 VA hospitals that offered referral to either home-based CR or facility-based CR vs. 121 VA hospitals that offered referral to only facility-based CR (usual care). Results: The number of VA medical centers offering home-based CR increased from 2 in 2010 to 13 in 2015. Among the 20,949 eligible patients hospitalized at VA medical centers that implemented home-based CR between 2010 and 2015, CR participation increased from 11% to 26% (Figure). Among the 85,328 eligible patients hospitalized at VA medical centers that did not offer home-based CR, CR participation increased from only 8% to 11%. Conclusion: Among eligible patients with IHD, participation in CR more than doubled at VA medical centers that implemented home-based CR programs between 2010 and 2015, whereas participation increased by only 3% at VA medical centers that did not implement home-based CR programs. Home-based CR is an effective way of engaging patients who may otherwise decline to participate in CR.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".