Impact of 2 different aerobic periodization training protocols on left ventricular function in patients with stable coronary artery disease: an exploratory study
Bibliographic record
Abstract
We compared the impacts of linear (LP) and nonlinear (NLP) aerobic training periodizations on left ventricular (LV) function and geometry in coronary artery disease (CAD) patients. Thirty-nine CAD patients were randomized to either a 3-month isoenergetic supervised LP or NLP. All underwent standard echocardiography with assessment of 3D LV ejection fraction (LVEF), diastolic function, strain (global longitudinal, radial, and circumferential), and strain rate at baseline and study end. Training was performed 3 times/week and included high-intensity interval and moderate-intensity continuous training sessions. Training load was progressively increased in the LP group, while it was deeply increased and intercepted with a recovery week each fourth week in the NLP group. For the 34 analyzed patients, we found similar improvements for 3D LVEF (effect size (ES): LP, 0.29; NLP, 0.77), radial strain (ES: LP, 0.58; NLP, 0.48), and radial strain rate (ES: LP, 0.87; NLP, 0.17) in both groups (time for all: p ≤ 0.01). All other parameters of cardiac function remained similar. In conclusion, NLP and LP led to similar improvements in 3D LVEF and radial strain, suggesting a favourable positive cardiac remodelling through myofibers reorganization. These findings must be investigated in patients with more severe cardiac dysfunction. The study was registered on ClinicalTrials.gov (NCT03443193). Novelty: Linear and nonlinear periodization programs improved radial strain, accompanied by improvement of ejection fraction. Both aerobic periodization programs did not negatively impact cardiac function in coronary artery disease patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".