Usefulness of the updated logistic clinical <scp>SYNTAX</scp> score after percutaneous coronary intervention in patients with prior coronary artery bypass graft surgery: Insights from the <scp>GLOBAL LEADERS</scp> trial
Bibliographic record
Abstract
OBJECTIVES: We aimed to investigate the prognostic utility of the anatomical CABG SYNTAX and logistic clinical SYNTAX scores for mortality after percutaneous coronary intervention (PCI) in patients with prior coronary artery bypass grafts (CABG). BACKGROUND: The anatomical SYNTAX score evaluated the anatomical complexity of coronary artery disease and helped predict the prognosis of patients undergoing PCI. The anatomical CABG SYNTAX score was derived from the anatomical SYNTAX score in patients with prior CABG, whilst the logistic clinical SYNTAX score was developed by incorporating clinical factors into the anatomical SYNTAX score. METHODS: We calculated the anatomical CABG SYNTAX score and logistic clinical SYNTAX score in 205 patients in the GLOBAL LEADERS trial. The predictive abilities of these scores for 2-year all-cause mortality were evaluated. RESULTS: Using the median scores as categorical thresholds between low and high score groups, the logistic clinical SYNTAX score was able to discriminate the risk of 2-year mortality, unlike the anatomical CABG SYNTAX score. The logistic clinical SYNTAX was significantly better at predicting 2-year mortality, compared to the anatomical CABG SYNTAX score, as evidenced by AUC values in receiver-operating characteristic curve analysis (0.806 vs. 0.582, p < .001) and integrated discrimination improvement (0.121, p < .001). CONCLUSIONS: The logistic clinical SYNTAX score was superior to the anatomical CABG SYNTAX score in predicting 2-year mortality.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".