Exercise capacity and body mass index are more important than diabetes in the prediction of high risk markers on myocardial perfusion imaging
Bibliographic record
Abstract
544 Objectives Diabetes has been identified as a high risk marker for coronary artery disease and hard cardiac events (unstable angina, nonfatal myocardial infarction, or cardiac death). Exercise capacity has been associated with a decreased rate of hard cardiac events. This study evaluates whether diabetics, after controlling for exercise capacity, are any more likely than non-diabetics to have high risk markers on myocardial perfusion imaging. Methods Consecutive patients referred for outpatient stress myocardial perfusion imaging for known or suspected coronary artery disease were evaluated. Bruce protocol treadmill stress with gated myocardial perfusion SPECT imaging was performed. Semiquantitative analysis was performed using a 17 segment, 5-point scale to obtain summed stress and difference scores (SSS, SDS). Results A total of 1987 patients were evaluated. Diabetes and exercise capacity were both correlated with SSS, SDS, left ventricular ejection fraction (LVEF), and chronotropic response (diabetes associated with a higher risk and exercise capacity with a lower risk). After controlling for exercise capacity, the only remaining significant correlation with diabetes was a lower LVEF. On the other hand, after controlling for diabetes, exercise capacity continued to be significantly and negatively correlated with all high risk markers. Significant predictors of SDS using linear regression were age, sex, and exercise time but not diabetes. Conclusions In the outpatient setting, exercise capacity is more strongly correlated with myocardial perfusion imaging results than is diabetes. Findings raise the possibility that a preserved exercise capacity may be able to overcome some of the adverse clinical consequences commonly associated with diabetes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".