Simple treadmill score to diagnose coronary disease. Chest 119:1933–1940
Bibliographic record
Abstract
Objective: Our aim was to derive and validate a simplified treadmill score for predicting the probability of angiographically confirmed coronary artery disease (CAD). Background: The American College of Cardiology/American Heart Association guidelines for exercise testing recommend the use of multivariable equations to enhance the diagnostic characteristics of the standard treadmill test. Most of these equations use complicated statistical techniques to provide diagnostic estimates of CAD. Simplified scores derived from such equations that require physicians only to add points have been developed for pretest estimates of disease and for prognosis. However, no simplified score has been developed specifically for the diagnosis of CAD using exercise test results. Methods: Consecutive patients referred for evaluation of chest pain who underwent standard treadmill testing followed by coronary angiography were studied. A logistic regression model was used to predict clinically significant (> 50 % stenosis) CAD and then the variables and coefficients were used to derive a simplified score. The simplified score was calculated as follows: (6 max-imal heart rate code) (5 ST-segment depression code) (4 age code) angina pectoris code hypercholesterolemia code diabetes code treadmill angina index code. The
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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 | 0.000 | 0.000 |
| 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) | 0.029 | 0.001 |
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".