Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide. Advancements in acute CVD treatments have resulted in high prevalence of patients living with CVD who are at high risk of recurrence and mortality. This burden of CVD has been great in high-income countries for decades, and is now reaching epidemic proportions in low and middle-income countries (LMICs). Cardiac rehabilitation (CR) is an outpatient model of chronic disease management for secondary CVD prevention. Robust evidence demonstrates that CR participation reduces mortality by 25%, morbidity and CVD risk factors and improves quality of life, all in a cost-effective manner.1 Hence, CR serves as a key tool in addressing the global burden of CVD. Despite clinical practice guideline recommendations for CR, it is underused globally. The reasons for CR underuse are well known, and include factors from the patient-level through to the healthcare system writ large. Arguably, the most important factors explaining CR underutilisation are geographic access, cost, patient time conflicts during work hours due to role obligations, and lack of awareness regarding the nature of CR and the associated benefits. To overcome these barriers, alternative models of CR delivery have been developed—most notably home-based CR. Home-based CR involves delivery of all the core components of traditional CR; however, patients are supported in their education and are provided counselling over the phone, and they engage in their prescribed exercise in an unsupervised setting. Participation in home-based CR is associated with equivalent benefits to supervised programmes in a cost-effective manner. With advances in technology, hybrid home-based programmes have been developed, incorporating email communication between patients and CR providers, telehealth videoconferencing with remote patients, and logging of physical activity on secure CR websites, for example. Most recently, CR has been delivered via mobile phones. Indeed, the article by Varnfield et al 2 describes …
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.096 | 0.036 |
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".