P4597Benefit of medical therapy versus revascularization in patients with stress myocardial perfusion single photon emission computed tomography: results from a large international registry
Bibliographic record
Abstract
Background: The impact of inducible ischemia by stress myocardial perfusion single photon emission computed tomography (MPS) on major adverse cardiovascular events (MACE) is not clearly defined, neither the benefit of revascularization. Purpose: We sought to characterize the benefit of early revascularization versus medical therapy in patients with inducible ischemia by automated quantification of total perfusion deficit (TPD). Methods: A total of 20,441 consecutive patients from a multi-center international cohort who underwent exercise or adenosine MPS test with 99mTc-sestamibi on latest generation scanners were included. The patients with a history of myocardial infarction, revascularization, cardiac transplant, or open heart surgery were excluded (n=5,807) leaving a final population of 14,634 patients. The studied population were divided into 2 groups: patients who underwent early revascularization, defined as percutaneous coronary intervention or bypass surgery within 90 days after MPS test, and those who were treated medically. Since patients with prior myocardial infarction were excluded from the study, the magnitude of myocardial ischemia was assumed to be measured by quantitative stress TPD measures. A propensity score was developed using logistic regression analysis to adjust for nonrandomization of treatment. A Cox proportional hazards model was used to predict MACE (death, myocardial infarction, and unstable angina) based on propensity for early revascularization.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".