Cost-effectiveness of digitally enabled cardiac rehabilitation: progress, promise, and persisting questions
Bibliographic record
Abstract
This editorial refers to ‘Cost-effectiveness of a digitally enabled cardiac rehabilitation programme for patients with coronary heart disease’, by J. Braver et al. https://doi.org/10.1093/eurjpc/zwaf512. Cardiac rehabilitation (CR) improves clinical outcomes in patients with coronary heart disease, yet uptake remains persistently low despite decades of evidence.1 Barriers to CR are multifactorial, with frequently reported patient-related barriers including long distances to the CR centre, limited transport options, and scheduling difficulties.2 Digitally enabled cardiac rehabilitation (DeCR), delivered through telehealth consultations and mobile applications, has been shown to be non-inferior to traditional face-to-face CR and offers a potential solution to the persistent challenges of access and adherence.3 In this issue of the European Journal of Preventive Cardiology, Braver et al.4 present a cost-effectiveness analysis of a DeCR programme vs. usual care (face-to-face CR or no CR) in patients after hospitalization for coronary heart disease. The study relied on observational claims data rather than data from a randomized controlled trial, analysing national private insurance claims from 337 patients in Australia. Eighty-eight patients participated in DeCR, while propensity score matching was used to select 249 patients receiving usual care, of whom 85 attended face-to-face CR and 164 received no CR. The intervention consisted of an 8-week remote programme that combined weekly 30-min telehealth consultations with a mobile application designed to support risk-factor management, personalized education, reminders, and behavioural change.
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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 | 0.013 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.032 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".