Angiographic Correlates of the Treadmill Scores in Non-High-Risk Patients with Unstable Angina
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it