The Evaluation of Duke Treadmill Score in the Risk Stratification of Coronary Heart Disease Patients
Bibliographic record
Abstract
Objective:To evaluate the efficacy of the Duke treadmill score(DTS)for risk-stratification of significant or severe coronary heart diease.Methods:One hundred and fifiy-one patients with chest pain undergoing electrocardiogram treadmill exercise test(ETT)and coronary angiography were enrolled,and those who were divided moderate-risk DTS group(score-10～+4,n=65)and high-risk DTS group(score≤-11,n=86).The clinical data,ETT data and coronary angiography results were compared between two groups.CrosstAbs Chi-Square Test was conducted to predict signitycant(least 1-vessel ≥50% stenosis)and sever(3-vessel or left main)coronary heart disease.Results:In high-risk group had more ST deviation,higher ST/HR-index,more exercise-limiting angina patients and average annual mortality;but less non-angina,less exercise time,lower workload than the moderate-risk group(all P0.001).Along with the extent of coronary artery disease was gravity,the high-risk group patients were increased.Positive coronary angiography significant(least 1-vessel ≥50% stenosis)patients 44(67.7%)and 80(93.0%),sever(3-vessel or left main)coronary artery disease patients 9(13.8%)and thirtieth-nine patients(45.3%)for moderate-and high-risk DTS groups respectively(all P0.001).Predicted average annual mortality was 2.9% and 6.8% for moderate-and high-risk DTS groups respectively(P0.001).Conclusions:The composite DTS not only provides accurate prognostic estimation but also predicts the extent of coronary artery disease.Utilizing DTS can help to improve the risk evaluation of coronary heart disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".