Risk Assessment by Myocardial Perfusion Imaging for Coronary Revascularization, Medical Therapy, and Noncardiac Surgery
Bibliographic record
Abstract
Stress myocardial perfusion imaging (MPI) has become an important tool in risk stratification of patients with known coronary artery disease. A normal myocardial perfusion scan has a high negative predictive value and is associated with low annual mortality rate (< 1%). Patients with extensive ischemia (> 20% of the left ventricle), defects in more than 1 coronary vascular territory, transient or persistent left ventricular cavity dilation, and ejection fraction less than 45% have a high annual mortality rate (> 3%). Those patients should undergo coronary revascularization whenever feasible, as the cardiac event rate increases in proportion to the magnitude of the jeopardized myocardium. Stress MPI can be used to demonstrate ischemia in patients with symptoms early after coronary artery bypass surgery (< 5 years) or in those without symptoms late (>/= 5 years) after coronary artery bypass surgery. With respect to patients who underwent percutaneous interventions, stress MPI can help detect in-stent restenosis early after the intervention (3-6 months) or assess the progression of native coronary disease afterward. Since preliminary data suggest that a reduction in the perfusion defect size may translate to a reduction of coronary events, stress MPI can help assess the efficacy of medical management of coronary disease. Finally, stress MPI is indicated for perioperative cardiac risk stratification for noncardiac surgery in patients with intermediate risk predictors (mild angina, prior myocardial infarction or heart failure symptoms, diabetes mellitus, renal insufficiency) and poor functional capacity or in those who undergo high-risk surgery with significant implications in further preoperative management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".