The Influence of Endothelial Function and Myocardial Ischemia on Peak Oxygen Consumption in Patients with Coronary Artery Disease
Bibliographic record
Abstract
Impaired endothelial function has been shown to limit exercise in coronary artery disease (CAD) patients and has been implicated in myocardial ischemia. However, the association of endothelial function and ischemia on peak exercise oxygen consumption (VO(2)) has not been previously reported. A total of 116 CAD patients underwent standard exercise stress testing, during which VO(2) was measured. On a separate day, endothelial-dependent and -independent function were assessed by ultrasound using flow-mediated arterial vasodilation (FMD) and sublingual glyceryl trinitrate administration (GTNMD) of the brachial artery. Patients with exercise-induced myocardial ischemia had lower FMD than nonischemic patients (3.64 ± 0.57 versus 4.98 ± 0.36, P = .050), but there was no difference in GTNMD (14.11 ± 0.99 versus 15.47 ± 0.63, P = .249). Analyses revealed that both FMD (P = .006) and GTNMD (P = .019) were related to peak VO(2). However, neither the presence of ischemia (P = .860) nor the interaction of ischemia with FMD (P = .382) and GTNMD (P = .151) was related to peak VO(2). These data suggest that poor endothelial function, potentially via impaired NO production and smooth muscle dysfunction, may be an important determinant of exercise capacity in patients with CAD, independent of myocardial ischemia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".