Is new science driving practice improvements and better patient outcomes? Applications for cardiac rehabilitation
Bibliographic record
Abstract
Evidence from many clinical trials in recent years suggests that a large "treatment gap" exists between recommended therapies and the care that patients actually receive. This gap has been particularly apparent in the area of primary and secondary prevention of cardiovascular disease. In this article, three areas are discussed in which new scientific advances have not been adequately translated to clinical practice. These include: 1) the most appropriate measures to define the risks associated with obesity; 2) the under-diagnosis of obstructive sleep apnea and its relation to cardiovascular risk; and 3) the use and misuse of the exercise test and other functional status tools to predict health outcomes. Each is discussed in terms of how they should be quantified, their contribution to the estimation of cardiovascular disease risk, their response to interventions, and implications for cardiac rehabilitation. Clinical cardiac rehabilitation programs can benefit from routinely including these measures, both for their value in stratifying risk and for their importance in quantifying program efficacy. Physicians and allied health professionals should expand their routine medical evaluations and coronary risk factor profiling to include these measures.
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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 | 0.074 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".