Left main coronary stenosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Coronary artery bypass grafting (CABG) has been regarded as the mainstream treatment for unprotected left main coronary artery (ULMCA) stenosis. However, the results of the Evaluation of XIENCE versus Coronary Artery Bypass Surgery for Effectiveness of Left Main Revascularization (EXCEL) trial, in which percutaneous coronary intervention (PCI) was deemed noninferior to CABG, have raised a question whether the guidelines should be changed. This article provides a critical appraisal of recent randomized control trials (RCTs) on ULMCA stenosis. RECENT FINDINGS: In contrast to EXCEL trial, another large RCT named the Nordic-Baltic-British Left Main Revascularization trial showed that PCI is inferior to CABG in patients treated for ULMCA stenosis. The reason for the discrepancy between these two RCTs may be due to differences in study design. In EXCEL trial, the adoption of new periprocedural myocardial infarction definition, the noninclusion of target vessel revascularization as a primary endpoint component, and the timeline of the study may have helped claim that PCI is noninferior to CABG. SUMMARY: The long-term efficacy of PCI for ULMCA stenosis has not yet been demonstrated. Further studies and follow-up data are needed before the indications for PCI are expanded in this scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".