Cardiac hemodynamic responses to exercise training in patients with coronary heart disease
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background In coronary heart disease (CHD) patients, tailored training programs are strongly recommended to decrease cardiovascular mortality but factors responsible for a positive response to training remain unclear. The aim of this study was to compare cardiac hemodynamic parameters changes after a training program in CHD patients, according to the training responder’s status. Methods 72 CHD patients (78% males, 63±9y) were recruited to perform a 12-week training consisting in a combination of endurance and endurance sessions. Patients underwent 2 cardiopulmonary exercise testing (CPET) using a ramp protocol (10 to 15 W/min) on a cycle ergometer pre- and post-training. Hemodynamic parameters (e.g., cardiac output (CO), cardiac index (CI), stroke volume (SV), systemic vascular resistance (SVR)) were measured by bioimpedance, and CPET parameters (e.g., peak oxygen uptake (O2peak)) were also measured pre- and post-training. Results Patients were divided in 2 groups (responders-R and non-responders-NR) according to the median change in O2peak. In the R group, O2peak (+16.8%, p < 0.001), CO, CI and SV increased by16.6%, 16.8% and 13.0%, respectively (p < 0.01) after the training program. In the NR group, O2peak, CO, CI and SV increased by 0.48%, 5.12%, 8.07% and 5.75%, respectively (p < 0.01). The SVR decreased in both groups (-19.0% in R and -11.4% in NR, p < 0.001). Conclusion The R group of CHD patients showed better improvement in cardiac hemodynamic and CPET parameters after a 12-week training program than NR. Further studies are needed to determine the best way to individualize training programs for the non-responders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".