The Relationship Between Quantitative Ischemia, Early Revascularization, and Major Adverse Cardiovascular Events
Bibliographic record
Abstract
Background: Observational data have suggested that patients with moderate to severe ischemia benefit from revascularization. However, this was not confirmed in a large, randomized trial. Objectives: Using a contemporary, multicenter registry, the authors evaluated differences in the association between quantitative ischemia, revascularization, and outcomes across important subgroups. Methods: Patients who underwent myocardial perfusion imaging in 12 centers were included in this retrospective analysis. The population was divided into original (2009-2014) and recent (2014-2021) registry sites. Early revascularization was defined as any revascularization within 90 days of myocardial perfusion imaging. A propensity score was developed to adjust for nonrandomization. Propensity score-adjusted survival analyses were used to evaluate the associations between quantitative ischemia, early revascularization, and death or myocardial infarction (MI) to identify at what severity of ischemia the HR for early revascularization crosses 1 (threshold for potential benefit). Results: < 0.001). The threshold for ischemia, above which patients may benefit from revascularization, was higher in more recent patients (14.0% vs 6.5%), but similar in female (>10.0%) and male patients (>8.6%). Conclusions: Early revascularization was associated with reduced risk in patients with a higher burden of quantitative ischemia in more recent populations. These findings suggest that methods integrating more factors than just ischemia are needed to improve patient selection for 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 | 0.003 | 0.011 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".