Changing the lexicon of ‘Cardiac Rehabilitation’: a progressive step
Bibliographic record
Abstract
The questioning by Ting et al.1 of the use of the term ‘cardiac rehabilitation’ is insightful and timely. Continued use of this term is at odds with evolving modern risk reduction programmes, exemplified by diabetes care. The term ‘rehabilitation’ conveys an intervention from which the patient is ‘rehabilitated’. Rather than a time-limited rehabilitation period, contemporary coronary heart disease (CHD) management seeks to reduce cardiovascular risk via life-long behaviour change and ongoing care. However, revising the macro-label alone fails to recognize the severity and underlying causes of the low use of cardiac rehabilitation. From the plethora of guidelines and position statements, it is easier to prescribe pharmacotherapy or perform interventional procedures than to encourage participation in a continuing disease management programme such as cardiac rehabilitation; or is it? Ting et al.1 suggest that cardiac rehabilitation is considered less important by mainstream cardiology because patients are unaware of its existence, benefits, and relevance. Whilst true, it also must also be recognized that large numbers of patients who are aware of its existence and benefits still decide not to participate.2,3 Utilization of centre-based cardiac rehabilitation services is strongly influenced by many additional factors, among them the large distance which most patients have to travel to programmes and their sex and age.2,3
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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 | 0.027 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.065 | 0.143 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".