New Insights in the Prescription of Exercise for Coronary Patients
Bibliographic record
Abstract
Prescribing exercise for cardiac patients is comparable in many ways to prescribing medications; that is, one recommends an optimal dosage according to individual needs and clinical status. Recent research has shown that it is more accurate to prescribe exercise as a percentage of the oxygen uptake reserve (VO2R), which is the difference between resting and maximal or peak oxygen consumption, rather than as a percentage of the VO2 max. Moreover, it appears that a minimum of 1600 kcal/week of leisure-time physical activity may halt the progression of coronary artery disease, whereas regression may be achieved with a gross energy expenditure of 2200 kcal/week. Upper body and resistance training have also been shown to be safe and effective for clinically stable patients. Aerobic capacity serves as an independent predictor of all cause and cardiovascular mortality in patients referred to an outpatient cardiac rehabilitation program, with each 1 metabolic equivalent increase in aerobic fitness conferring an approximate 10% reduction in mortality. The goal of preventing recurrent cardiac events is, to a large extent, based on sustained compliance to multifactorial interventions, which can be influenced by numerous socioeconomic and clinical variables, and enhanced by home-based or group cardiac rehabilitation programs that are designed to circumvent or attenuate barriers to participation and adherence, so that many more individuals may realize the benefits that secondary prevention can provide.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".