Additional file 1 of The effect of diabetes on surgical versus percutaneous left main revascularization outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1. Table S1. The PRISMA 2020 27-item checklist for reporting in systematic reviews and meta-analyses. Table S2. The detailed search strategy for PubMed, Embase and the Cochrane Central Register of Controlled Trials (CENTRAL). Table S3. Detailed definitions of key outcomes from the four included randomized controlled trials (Morice et al. 2014, Milojevic et al. 2019, Holm et al. 2020, and Park et al. 2020). Table S4. Detailed evaluation of the risk of bias for the randomized controlled trials using the Cochrane’s Collaboration risk-of-bias (RoB 2) tool. Table S5. Detailed evaluation of the risk of bias for the observational studies using the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool. Figure S1. Random-effects meta-analysis testing for effect measure modification by diabetes comparing DES to CABG using relative risks for all-cause mortality. 1=DM, 0 = non-DM; ES, estimate; CI, confidence interval. Figure S2. Random-effects meta-analysis testing for effect measure modification by diabetes comparing DES to CABG using relative risks for all-cause mortality, myocardial infarction, or stroke. 1 = DM, 0 = non-DM; ES, estimate; CI, confidence interval. Figure S3. Random-effects meta-analysis testing for effect measure modification by diabetes comparing DES to CABG using relative risks for revascularization. 1 = DM, 0 = non-DM; ES, estimate; CI, confidence interval. Figure S4. Fixed effects meta-analysis comparing DES to CABG in diabetic patients using relative risks for the composite endpoint of all-cause mortality, myocardial infarction, stroke, or unplanned revascularization. 1 = DM, 0 = non-DM; ES, estimate; CI, confidence interval. Figure S5. Influence analysis with each study being excluded in turn. 1, Zhao 2011; 2, Meliga 2013; 3, Yu 2015; 4, Zheng 2016; 5, Lee 2017; 6, Lee 2020. Figure S6: Funnel plot of the observational studies for the composite endpoint of all-cause mortality, myocardial infarction, or 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 | 0.006 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.840 | 0.046 |
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".