Post hoc assessment of the relationship among coronary stenosis, electrocardiography, and ventricular function in patients with heart disease
Bibliographic record
Abstract
Cardiovascular diseases including cardiac arrhythmias lead to fatal events in patients with coronary artery disease; however, clinical associations from echocardiography, electrocardiography (ECG), and biomarkers remain unknown. We sought to identify the factors that may be related to elevated QRS intervals in patients with risk for coronary artery disease. In this study, we performed analysis of clinical data from 503 patients divided into two groups, i.e., patients with either <50% coronary artery stenosis or >50% coronary artery stenosis. We further examined patients with elevated ECG parameters such as QRS > 100 ms and QTc > 440 ms. Patients with >50% coronary artery stenosis exhibited significant increases in age, triglycerides, and troponin levels. Further, ECG parameters demonstrated increased QRS and QTc durations, while echocardiographic parameters highlighted a decrease in ejection fraction (EF) and fractional shortening (FS). Patients with QTc > 440 ms exhibited increased brain natriuretic peptide and creatinine levels with a decrease in estimated glomerular filtration rate clearance rates. Patients with QRS > 100 ms had greater left ventricular (LV) mass and LV internal diameter in systole and diastole. Multimodal logistic regression showed significant relation between QTc, age, and creatinine. These findings suggest that patients with significant coronary stenosis may have lower EF and FS with prolonged QRS intervals, demonstrating greater risk for arrhythmic events.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".