Cardiac hemodynamics phenotypes and individual responses to training in coronary heart disease patients
Bibliographic record
Abstract
Abstract Background In patients with coronary heart disease (CHD), individualized exercise training (ET) programs are strongly recommended to optimize peak oxygen uptake (O 2peak ) improvement and prognosis. However, the cardiac hemodynamic factors responsible for a positive response to training remain unclear. The aim of this study was to compare cardiac hemodynamic changes after an ET program in responder (R) versus non‐responder (NR) CHD patients. Methods A total of 72 CHD patients completed a 3‐month ET program and were assessed by cycle ergometer cardiopulmonary exercise test (CPET: O 2peak assessment) with impedance cardiography (ICG) for hemodynamic measurements before and after training. Cardiac hemodynamics (e.g., CO, CI, SV, ESV, EDV, and SVR) were measured by ICG during CPET. The R and NR groups were classified using the median change in O 2peak (>the median for R and ≤the median for NR). Results In the R group, O 2peak (+17%, p < 0.001), CO, CI, SV, and HR increased by 17%, 17%, 13%, and 5%, respectively ( p < 0.05) after the training program. In the NR group, O 2peak , CO, CI, and SV increased by 0.5%, 5%, 8%, and 6%, respectively ( p < 0.01). The SVR decreased in both groups (−19% in R and −11% in NR, p < 0.001). Conclusion Among CHD patients, the R group showed a better improvement in peak cardiac output via an increase in peak stroke volume and heart rate and a reduced systemic vascular resistance than the NR group. Different cardiac phenotype adaptations and clinical individual responses were identified in CHD patients according to the aerobic fitness responder's status.
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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".