Effect of Side Branch Predilation in Coronary Bifurcation Stenting With the Provisional Approach ― Results From the COBIS (Coronary Bifurcation Stenting) II Registry ―
Bibliographic record
Abstract
BACKGROUND: Whether side branch (SB) predilation before main vessel (MV) stenting is beneficial is uncertain, so we investigated the effects of SB predilation on procedural and long-term outcomes in coronary bifurcation lesions treated using the provisional approach. METHODS AND RESULTS: A total of 1,083 patients with true bifurcation lesions undergoing percutaneous coronary intervention were evaluated. The primary outcome was a major adverse cardiovascular event (MACE): cardiac death, myocardial infarction, or target lesion revascularization. SB predilation was performed in 437 (40.4%) patients. Abrupt (10.5% vs. 11.3%; P=0.76) or final SB occlusion (2.7% vs. 3.9%; P=0.41) showed no differences between the predilation and non-predilation groups. The rates of angiographic success (69.1% vs. 52.9%, P<0.001) and SB stent implantation (69.1% vs. 52.9%, P<0.001) were significantly higher in the predilation group. During a median follow-up of 36 months, we found no significant difference between the groups in the rate of MACE (9.4% vs. 11.5%; P=0.67) in a propensity score-matched population. In subgroup analysis, patients with minimal luminal diameter of the parent vessel ≤1 mm benefited from SB predilation in terms of preventing abrupt SB occlusion (P for interaction=0.04). CONCLUSIONS: For the treatment of true bifurcation lesions, SB predilation improved acute angiographic and procedural outcomes, but could not improve long-term clinical outcomes. It may benefit patients with severe stenosis in the parent vessel.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".