Availability and Delivery of Cardiac Rehabilitation in South-East Asia
Bibliographic record
Abstract
Background: The aims of this study were to establish cardiac rehabilitation (CR) availability and density, as well as the nature of programs in South-East Asian Region (SEAR) countries, and to compare this with other regions globally. Methods: In 2016/2017, the International Council of Cardiovascular Prevention and Rehabilitation engaged cardiac associations to facilitate program identification globally. An online survey was administered to identify programs using REDCap, assessing capacity and characteristics. CR density was computed using Global Burden of Disease study annual ischemic heart disease (IHD) incidence estimates. The program audit was updated in 2020. Results: CR was available in 6/11 (54.5%) SEAR countries. Data were collected in 5 countries (83.3% country response); 32/69 (68.1% response rate from 2016/2017) programs completed the survey. These data were compared to 1082 (32.1%) programs in 93/111 (83.3%) countries with CR. Across SEAR countries, there was only one CR spot per 283 IHD patients (vs. 12 globally), with an unmet regional need of 4,258,968 spots annually. Most programs were in tertiary care centers (n = 25, 78.1%; vs. 46.1% globally, P < 0.001). Most were funded privately (n = 17, 56.7%; vs. 17.9%, P < 0.001), and 22 (73.3%) patients were paying out of pocket (vs. 36.2% globally; P < 0.001). The mean number of staff on the multidisciplinary teams was 5.5 ± 3.0 (vs. 5.9 ± 2.8 globally P = 0.268), offering 8.6 ± 1.7/11 core components (consistent with other countries) over 16.8 ± 12.6 h (vs. 36.2 ± 53.3 globally, P = 0.01). Conclusion: Funded CR capacity must be augmented in SEAR. Where available, services were consistent with guidelines, and other regions of the globe, despite programs being shorter than other regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".