Evaluation of ST‐segment changes during and after maximal exercise tests in one‐, two‐ and three‐vessel coronary artery disease
Bibliographic record
Abstract
OBJECTIVE: To relate ECG and heart rate (HR) variables during and after exercise testing with the presence of one-, two- or three-vessel disease defined by angiography. DESIGN: Seventy-three male patients with stable angina pectoris and angiographically verified coronary artery disease underwent a maximal exercise test. From 12-lead ECG recordings and computer averaging ST-amplitude and HR data were measured in consecutive 10-s intervals. RESULTS: In univariate analysis, patients with three-vessel disease had lower maximal exercise capacity, a shorter time to >1 mm ST-depression, more often a clockwise ST/HR recovery loop, more frequently a post-exercise downward ST-segment slope, and a greater ST-deficit at 3.5 min after exercise than patients with one-vessel disease. In multivariate analysis, time to >1 mm ST-depression discriminated between patients with three- and one-vessel disease. In patients with an intermediate time to >1 mm ST-depression a clockwise ST/HR recovery loop and/or a downsloping ST-segment in the post-exercise period were significantly more prevalent in severe vessel disease. CONCLUSION: Patients with three-vessel disease had a significantly shorter time to >1 mm ST-depression during exercise and more often an abnormal post-exercise ST/HR reaction than those with one-vessel disease.
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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 | 0.002 |
| 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) | 0.001 | 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".