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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".