Risk stratification of patients undergoing medical therapy after coronary angiography
Bibliographic record
Abstract
AIM: We sought to investigate the prognostic impact of the SYNTAX (Synergy between PCI with TAXUS and Cardiac Surgery) score (SS) on 1-year clinical outcomes in patients with non-ST-segment elevation acute coronary syndromes (NSTE ACS) undergoing medical therapy only. METHODS AND RESULTS: Among the 13 819 patients enrolled in the ACUITY trial and undergoing coronary angiogram, 4491 patients were treated with medical therapy as the initial strategy. Of those, baseline SS and complete angiographic analysis were available in 1275 patients. Patients were divided in four groups based on the presence or absence of coronary artery disease (CAD) and subsequently, among patients with CAD, by SS. Major adverse cardiac events (MACE) and its individual components (death, myocardial infarction, and unplanned revascularization) were compared between groups. Among the 1275 patients, the mean SS was 3.5 ± 7.0 (range 0-45). SYNTAX score was 0 in 842 patients, >0 and ≤5 in 170, >5 and ≤11 in 119, and >11 in 144 patients. The 1-year rates of MACE were higher in patients with CAD and higher SS. By multivariable analysis, the SS was a strong predictor of all adverse ischaemic events, including mortality. By receiver operator characteristic analysis, an SS cut-off of 8 showed the best prognostic accuracy for death and MACE. CONCLUSION: In patients with NSTE ACS undergoing medical therapy, the SS, especially when >8, was shown to be a strong predictor of 1-year MACE, including mortality. This finding has important clinical implications for risk stratification of patients with NSTE ACS undergoing medical therapy after an initial angiogram.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".