γ‐Glutamyl Transferase and Long‐Term Survival in the SYNTAXES Trial: Is It Just the Liver?
Bibliographic record
Abstract
BACKGROUND: Recently, machine learning algorithms have identified preprocedural γ-glutamyl transferase (GGT) as a significant predictor of long-term mortality after coronary revascularization in the SYNTAX (Synergy Between PCI [Percutaneous Coronary Intervention] With Taxus and Cardiac Surgery) trial. The aim of the present study is to investigate the impact of preprocedural GGT on 10-year all-cause mortality in patients with complex coronary artery disease after revascularization. METHODS AND RESULTS: The SYNTAX trial was a randomized trial comparing PCI with coronary artery bypass grafting in 1800 patients with complex coronary artery disease. The present report is a post hoc subanalysis of the SYNTAXES (Synergy Between PCI With Taxus and Cardiac Surgery Extended Survival) trial, an investigator-driven extended 10-year follow-up of the SYNTAX trial. The association between preprocedural GGT and 10-year all-cause mortality was investigated. The mean values of GGT for men and women were 43.5 (SD, 48.5) and 36.4 (SD, 46.1) U/L, respectively. In multivariable Cox regression models adjusted by traditional risk factors, GGT was an independent predictor for all-cause death at 10-year follow-up, and each SD increase in log-GGT was associated with a 1.24-fold risk of all cause death at 10-year follow-up (95% CI, 1.10-1.40). According to previously reported sex-related GGT thresholds, patients with higher GGT level had a 1.74-fold risk of all-cause death at 10-year follow-up (95% CI, 1.32-2.29) compared with patients with lower GGT level. CONCLUSIONS: Preprocedural GGT is an independent predictor of 10-year mortality after coronary revascularization in patients with complex coronary artery disease. In patients with elevated GGT, strong secondary prevention may be required after revascularization and must be studied prospectively. REGISTRATION: URL: https://clinicaltrials.gov/study/NCT03417050.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".