Cardiac Rehabilitation in India: Results from the International Council of Cardiovascular Prevention and Rehabilitation’s Global Audit of Cardiac Rehabilitation
Bibliographic record
Abstract
Background: Cardiac rehabilitation (CR) is recommended in clinical practice guidelines for comprehensive secondary prevention. While India has a high burden of cardiovascular diseases (CVD), availability and nature of services delivered there is unknown. In this study, we undertook secondary analysis of the Indian data from the global CR audit and survey, conducted by the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR). Methods: In this cross-sectional study, an online survey was administered to CR programs, identified in India by CR champions and through snowball sampling. CR density was computed using Global Burden of Disease study ischemic heart disease (IHD) incidence estimates. Results: Twenty-three centres were identified, of which 18 (78.3%) responded, from 3 southern states. There was only one spot for every 360 IHD patients/year, with 3,304,474 more CR spaces needed each year. Most programs accepted guideline-indicated patients, and most of these patients paid out-of-pocket for services. Programs were delivered by a multidisciplinary team, including physicians, physiotherapists, among others. Programs were very comprehensive. Apart from exercise training, which was offered across all centers, some centers also offered yoga therapy. Top barriers to delivery were lack of patient referral and financial resources. Conclusions: Of all countries in ICCPR's global audit, the greatest need for CR exists in India, particularly in the North. Programs must be financially supported by government, and healthcare providers trained to deliver it to increase capacity. Where CR did exist, it was generally delivered in accordance with guideline recommendations. Tobacco cessation interventions should be universally offered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".