Effects of interval training in cardiovascular disease populations: a clinical perspective
Bibliographic record
Abstract
Nearly 20 years have elapsed since the first clinical trial investigated the impact of interval training on patients with cardiovascular disease (CVD). This clinical corner discusses the health outcomes of systematic reviews and meta-analyses and appropriately powered randomized clinical trials which have tested these interval training programs across various CVDs (i.e., coronary artery disease, heart failure, atrial fibrillation, peripheral arterial disease, and cardiac implantable electronic devices). The publications included in this clinical corner have shown that interval training leads to similar or superior improvements in peak oxygen uptake (V̇O2peak), functional capacity, pain free walking, quality of life, anxiety, depression, and endothelial function, but the magnitude of improvements across varying protocols (e.g., length and number of work periods, intensities of work periods, duration of exercise sessions, frequency of exercise sessions, and duration of program) and optimal dosage for males and females are unclear across CVD conditions. The heterogeneity in protocols, physical and mental health outcome measures, and lack of sex- and gender-based analyses calls for more high-quality research in this area.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".