Impact of lesion preparation strategies on outcomes of left main <scp>PCI</scp>: The <scp>EXCEL</scp> trial
Bibliographic record
Abstract
Abstract Objectives We examined outcomes according to lesion preparation strategy (LPS) in patients with left main coronary artery (LMCA) percutaneous coronary intervention (PCI) in the EXCEL trial. Background The optimal LPS for LMCA PCI is unclear. Methods We categorized LPS hierarchically (high to low) as: (a) rotational atherectomy (RA); (b) cutting or scoring balloon (CSB); (c) balloon angioplasty (BAL); and d) direct stenting (DIR). The primary endpoint was 3‐year MACE; all‐cause death, stroke, or myocardial infarction. Results Among 938 patients undergoing LMCA PCI, RA was performed in 6.0%, CSB 9.5%, BAL 71.3%, and DIR 13.2%. In patients treated with DIR, BAL, CSB, and RA, respectively, there was a progressive increase in SYNTAX score, LMCA complex bifurcation, trifurcation or calcification, number of stents, and total stent length. Any procedural complication occurred in 10.4% of cases overall, with the lowest rate in the DIR (7.4%) and highest in the RA group (16.1%) (ptrend = .22). There were no significant differences in the 3‐year rates of MACE (from RA to DIR: 17.9%, 20.2%, 14.5%, 14.7%; p = .50) or ischemia‐driven revascularization (from RA to DIR: 16.8%, 10.8%, 12.3%, 14.2%; p = .65). The adjusted 3‐year rates of MACE did not differ according to LPS. Conclusions The comparable 3‐year outcomes suggest that appropriate lesion preparation may be able to overcome the increased risks of complex LMCA lesion morphology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".