The value of the Clinical SYNTAX Score in predicting long-term prognosis in patients with ST-segment elevation myocardial infarction who have undergone primary percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: The Clinical SYNTAX Score (CSS) combines anatomical and clinical risk assessment. OBJECTIVES: This study was designed to evaluate CSS as a predictor of prognosis in patients with ST-elevation myocardial infarction (STEMI) undergoing a primary percutaneous coronary intervention (p-PCI). METHODS: We evaluated 433 patients who were diagnosed with STEMI and underwent p-PCI. CSS was calculated by multiplying the anatomically derived SYNTAX score (Sx) by the modified age, creatinine, and ejection fraction score. Patients were divided into tertiles according to the CSS: CSS(Low)≤14 (n=141), 14 26 (n=148). The primary endpoints were defined as all-cause mortality, myocardial infarction, and cerebrovascular events over 15 months' follow-up. RESULTS: Primary endpoints were achieved in 9.2% of patients with CSS≤14, 12.5% of those with 14 26 (P<0.001). Kaplan-Meier analysis showed that the CSS>26 group had a significantly higher incidence of primary endpoints [P (log-rank)<0.001]. CSS>26 was identified as an independent predictor for all-cause mortality, myocardial infarction, and cerebrovascular events (hazard ratio 3.58, 95% confidence interval 1.68-7.60, P=0.001). Receiver operating characteristic analysis found areas under the curve of 0.66, 0.59, and 0.64 for CSS, Sx score, and age, creatinine, and ejection fraction score (P<0.001, P=0.01, P<0.001, respectively). CONCLUSION: CSS may be better than the Sx score for predicting long-term prognosis in patients with STEMI undergoing p-PCI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".