Cost-effectiveness of cardiac rehabilitation program delivery models in patients at varying cardiac risk, reason for referral, and sex
Bibliographic record
Abstract
BACKGROUND: Little is known about the relative cost-effectiveness of different secondary prevention cardiac rehabilitation (CR) program designs or how cost-effectiveness is influenced by patient clinical and demographic characteristics. The purpose of the study was (i) to evaluate the incremental cost-effectiveness of a standard 3-month CR program (SCR) versus a program distributed over 12 months (distributed CR, DCR); and (ii) to determine the effect of patient demographic characteristics (cardiac risk, cardiac diagnosis, sex) on incremental cost-effectiveness. METHODS: A two group cost-effectiveness analysis was conducted alongside a randomized controlled trial. Patients with coronary artery disease (mean age=58 years, SD+/-10) were randomized to either SCR (n=196) or DCR (n=196) and followed for 24 months. Program delivery costs, cardiac healthcare use, morbidity, mortality, and quality-adjusted life years were assessed. Cost-effectiveness was evaluated with incremental cost-utility analysis. RESULTS: In the pooled analysis, we found the probability of SCR being more cost-effective than DCR was 63-67%. The subanalysis found SCR to be the more cost-effective intervention for patients at high risk, patients with previous coronary artery bypass graft and for male patients. The DCR program was more cost-effective for patients with lower risk of disease progression and for female patients. CONCLUSION: Differences were noted in the cost-effectiveness of CR models based on cardiac risk level, reason for referral, and demographic characteristics. Our results suggest improved cost-effectiveness may be gained by triaging patients to different CR intervention models, however, further investigation is required.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| 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) | 0.003 | 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".