Exercise Prescription and Proscription for Patients With Coronary Artery Disease
Bibliographic record
Abstract
E xercise training in patients with cardiovascular disease increases exercise capacity, [1][2][3] reduces cardiac ischemia, 1,2 delays the onset of or eliminates angina pectoris, 4,5 and improves endothelial function.6 Meta-analyses of exercisebased, cardiac rehabilitation studies have suggested that exercise training reduces cardiac mortality in coronary artery disease (CAD) patients.7-10 A recent randomized, controlled comparison of exercise training and angioplasty in selected patients with angina documented fewer cardiac events in the exercise subjects over the year of follow-up.2 Despite these benefits, exercise training is rarely prescribed for cardiac patients, as evidenced by the fact that only Ϸ20% 11,12 of qualified patients are referred to formal cardiac rehabilitation programs.Referral rates are even lower among women and older patients.13 The reasons for this underutilization are not defined but probably include health professionals' underestimation of the benefits of exercise, a lack of training in exercise therapeutics among many healthcare providers, poor financial reimbursement, the absence of reimbursed advocates for exercise therapy, 13 and the absence of a sufficiently large randomized clinical trial documenting a reduction in cardiac events.This review discusses the benefits of exercise training for patients with atherosclerotic cardiovascular disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".