Prediction of mortality in patients without angina Use of an exercise score and exercise echocardiography
Bibliographic record
Abstract
BACKGROUND: Exercise testing has limited efficacy for identifying coronary artery disease (CAD) in the absence of anginal symptoms. Exercise echocardiography is more accurate than standard exercise testing, but its efficacy in this situation has not been defined. We sought to identify whether the Duke treadmill score or exercise echocardiography (ExE) could be used to identify risk in patients without anginal symptoms. METHODS: We studied 1859 patients without typical or atypical angina, heart failure, or a history or ECG evidence of infarction or CAD, who were referred for ExE, of whom 1832 (age 51+/-15 years, 944 men) were followed for up to 10 years. The presence and extent of ischaemia and scar were interpreted by expert reviewers at the time of the original study. RESULTS: Exercise provoked significant (>0.1mV) ST segment depression in 215 patients (12%), and wall motion abnormalities in 137 (8%). Seventy-eight patients (4%) died before revascularization, only 17 from known cardiac causes. The independent predictors of death were age (RR 1.1, p<0.0001), smoking, Duke treadmill score (RR 0.9, p<0.0001) and resting LV dysfunction (RR 1.9, p<0.04), but did not include ischaemia at ExE. Echocardiography was not predictive of outcome in subgroups with an intermediate or high risk Duke score, nor in patients with two or more risk factors. CONCLUSIONS: Patients without anginal symptoms have a low mortality, especially from cardiac causes. If such individuals undergo exercise testing and a resting echocardiogram, exercise echocardiography does not offer additional prognostic information.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".