Cardiac rehabilitation and secondary prevention of CVD: time to think about cardiovascular health rather than rehabilitation
Bibliographic record
Abstract
During the past century, there have been major developments in the medical and surgical treatment of cardiovascular disease (CVD). These advancements have resulted in more people surviving initial events and having reduced length of stay in hospital; consequently, there is an increasing number of people in need of ongoing and lifelong cardiovascular risk management. The physical and emotional effects of living with CVD are ongoing with broad challenges ranging from the individual to system level. However, post-discharge care of people with coronary disease continues to follow a 50-year-old cardiac rehabilitation model which focuses on the sub-acute phase and is of a finite in duration. The aim of this paper is to consider the concept of supporting survivors to live well with CVD rather than 'rehabilitating' them and propose factors for consideration in reframing secondary prevention towards optimizing cardiovascular health. We discuss deeply-held potential considerations and challenges associated with the concept of supporting survivors achieve optimal cardiovascular health and live well with CVD rather than 'rehabilitating' them. We propose the concept of 5 x P's for reframing traditional cardiac rehabilitation towards the concept of cardiovascular health for survivors beyond 'rehabilitation'. These include the need for personalization, processes, patient-centered care, parlance, and partnership. Taken together, consideration of challenges at the systems and population level will ultimately improve engagement with secondary prevention as well as outcomes for all people who need it.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".