Long Term Prognostic Value Of SYNTAX Score II Among Stemi Patients—A Comprehensive Result From Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To assess the predictive value of SS II (SYNTAX score II) for long-term outcomes in ST-elevated myoarial infarction (STEMI) patients. Source: PubMed, EMBASE and Cochrane databases were searched up until September 24, 2021. Two investigators extracted data independently from the relevant articles. A random-effects model was conducted to combine the pooled hazard ratio (HR) or risk ratio (RR) for association between SS II and long term outcomes. Principal findings: A total of 12 articles (7,195 subjects) were included in the final meta-analyses. Analysis of nine of the articles showed that higher SS II predicted poor long term all-cause mortality among STEMI patients (pooled RRs=4.09,95%CI: 3.49-4.80). A similar association of SS II with poor long term mortality was observed when the crude HRs and adjusted HRs were pooled (crude HRs: pooled HR=1.07, 95%CI: 1.04-1.09; adjusted HRs: pooled HR=1.05, 95%CI:1.04-1.07). The STEMI patients with higher SS II also showed a higher associated with increased risk of long term major adverse cardiac events (pooled HR = 1.05, 95% CI: 1.02-1.07; pooled RR=2.28, 95%CI:2.02-2.57). A consistent association was found for heart failure among STEMI patients. Conclusion: Higher SS II predicted poor long term all-cause mortality, major adverse cardia events and heart failure among STEMI patients.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".