Design matters in secondary prevention: individualization and supervised exercise improves the effectiveness of cardiac rehabilitation
Bibliographic record
Abstract
BACKGROUND: Hospital or centre-based cardiac rehabilitation (CR) can lengthen and improve life. However, most existing trials do not examine the effects of design characteristics. To examine the effects of these characteristics, this study compared an individualized cardiac rehabilitation programme to a standardized programme and examined what factors contributed most to programme effects. DESIGN: A prospective cohort analysis was done comparing patients using an individualized centre-based cardiac rehabilitation programme (ICR) in a mixed urban-rural region of the west of Scotland, to a standardized cardiac rehabilitation programme (SCR) provided at the same site three years previously. Both inter- and intra-programme differences in outcomes were explored. RESULTS: More patients were referred to ICR than SCR (749 versus 414 patients, p = 0.002) and the proportion of patients who subsequently attended was around 30% higher (p < 0.0001) although the overall rate of referral to ICR was lower (70% versus 62%, p = 0.002). ICR was associated with a reduction in hospital admission compared to SCR (HR: 0.664: 95% confidence interval (CI) 0.554 to 0.797). ICR patients also had significantly shorter hospitalizations (mean: 8.02 days versus 5.84 days, p < 0.05). ICR patients who attended at least 75% of the exercise sessions were significantly less likely to be hospitalized than individuals who partially attended (HR 2.39, 95% CI: 1.659 to 3.488) or did not participate in exercise sessions (HR 2.16, 95% CI: 1.482 to 3.143). CONCLUSIONS: Individualized content and supervised exercise components are key design characteristics for improving outcomes from centre-based CR in clinically representative populations.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".