The Impact of SYNTAX Score with Longterm Outcomes of Coronary Artery Bypass Grafting and Percutaneous Coronary Intervention for Left Main Coronary Artery Disease: A Systematic Review
Bibliographic record
Abstract
The SYNTAX score was designed to predict the postprocedural risk associated with PCI or surgical revascularization. This study aims to evaluate long term (>5 years) outcome of PCI with CABG by comparing several existing studies. A systematic search was conducted through PubMed/MEDLINE and ScienceDirect databases. This systematic review used all studies in the field of differences in outcome of PCI and CABG intervention in left main coronary artery stenosis. This study used cross sectional study design with a systematic review method based on several cohort studies. The search was conducted until February 10th, 2023. Assessment of the study quality was carried out using the criteria of the Newcastle-Ottawa Scale (NOS). Based on the final assessment, all included studies had a mean NOS score of 8 which could be interpreted as excellent quality. Our search yielded 1675 studies of which we included 5 studies for the final analysis. A total of 5.494 patients were treated with PCI and CABG. In this study, there were differences outcome in low, medium and high syntax scores on long term major adverse cardiac or cerebrovascular events (MACCE) and long term mortality. However, the similar outcome was obtained in long term revascularization, long term stroke and long term Miokard Infark (MI). Left main coronary artery (LMCA) stenosis patients with low to high SS may result various long-term outcomes. CABG is associated with lower incidence of mortality, repeat revascularization, myocardial infarction, and MACCE than PCI. Meanwhile, PCI gives lower incidence of stroke.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".