Crush is superior to Culotte in two-stent strategy for treatment of left main coronary artery bifurcations: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Crush and Culotte techniques have been used increasingly to treat patients with complex unprotected left main coronary artery bifurcation lesions. This article compares published data on these two techniques. METHODS: Databases, including PubMed, Embase, Cochrane Library, Wanfang Data and China National Knowledge Infrastructure, were searched for articles published before Aug 21, 2019 to identify all relevant studies on left main coronary artery bifurcation lesions treated by Crush versus Culotte techniques. The pooled data were analyzed using either fixed- or random-effects model depending on heterogeneity (assessed via the I2 index). The endpoints were major adverse cardiac events, target lesion revascularization, cardiac death, stent thrombosis, myocardial infarction and target vessel revascularization. RESULTS: Eight articles with a total of 1,283 patients were included, and 710 patients were treated with Crush, and 573 ones with Culotte. Crush group was trend to decreased major adverse cardiac event compared with Culotte group [Relative ratio (RR) 0.63,95% confidence interval(CI) 0.39-1.04, I2 =72.7%], mainly driven by decreased cardiac death [RR 0.49, 95% CI(0.25-0.99), I2 =0%], decreased myocardial infarction [RR 0.40, 95% CI(0.21-0.76), I2 =21.6%],and lower stent thrombosis [RR 0.39, 95% CI(0.16-0.98), I2 =39.4%]. There was no significant difference in target lesion revascularization and target vessel revascularization between Crush and Culotte [RR 0.77, 95% CI 0.46-1.28, I2=61.1%; RR 0.78, 95% CI (0.30-2.02), I2 =73.1%, respectively]. CONCLUSION: Crush was superior to Culotte for treatment of left main coronary artery bifurcation lesions with a trend of lower incidence of long-term major adverse cardiac events, mainly derived from decreased myocardial infarction, stent thrombosis and cardiac death.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".