Angiographic Correlates of the Treadmill Scores in Non-High-Risk Patients with Unstable Angina
Bibliographic record
Abstract
BACKGROUND: There has been no clear consensus regarding the optimum definition of a high-risk exercise ECG test. The aim of this study is to compare the diagnostic accuracy of several treadmill scores [American College of Cardiology/American Heart Association (ACC/AHA) High-Risk Criteria for exercise testing, Duke Treadmill Score, Veterans Affairs and West Virginia Prognostic Score, ST/Heart Rate Index] with the ST-segment depression analysis in the detection of significant and severe coronary disease as determined by coronary angiography. METHODS: The study included a cohort of 248 consecutive patients admitted to hospital for unstable angina. RESULTS: The sensitivities of the ACC/AHA High-Risk Criteria and the ST depression > or =1 mm were 89.02 and 76.83%, respectively, for the detection of significant coronary artery disease, and 96.15 and 86.54% for the detection of severe coronary artery disease. The specificities of the Duke Treadmill Score and the ST depression > or=1 mm were 96.43 and 73.81%, respectively, for the detection of significant coronary artery disease, and 81.63 and 47.45% for the detection of severe coronary artery disease. CONCLUSIONS: The ACC/AHA High-Risk Criteria and Duke Treadmill Score provided relevant diagnostic information not available from the ST segment analysis alone.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".