Cardiac rehabilitation delivery model for low-resource settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease is a global epidemic, which is largely preventable. Cardiac rehabilitation (CR) is demonstrated to be cost-effective and efficacious in high-income countries. CR could represent an important approach to mitigate the epidemic of cardiovascular disease in lower-resource settings. The purpose of this consensus statement was to review low-cost approaches to delivering the core components of CR, to propose a testable model of CR which could feasibly be delivered in middle-income countries. METHODS: A literature review regarding delivery of each core CR component, namely: (1) lifestyle risk factor management (ie, physical activity, diet, tobacco and mental health), (2) medical risk factor management (eg, lipid control, blood pressure control), (3) education for self-management and (4) return to work, in low-resource settings was undertaken. Recommendations were developed based on identified articles, using a modified GRADE approach where evidence in a low-resource setting was available, or consensus where evidence was not. RESULTS: Available data on cost of CR delivery in low-resource settings suggests it is not feasible to deliver CR in low-resource settings as is delivered in high-resource ones. Strategies which can be implemented to deliver all of the core CR components in low-resource settings were summarised in practice recommendations, and approaches to patient assessment proffered. It is suggested that CR be adapted by delivery by non-physician healthcare workers, in non-clinical settings. CONCLUSIONS: Advocacy to achieve political commitment for broad delivery of adapted CR services in low-resource settings is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it