Percutaneous coronary intervention versus coronary artery bypass grafting
Bibliographic record
Abstract
PURPOSE OF REVIEW: The publication of the NOBLE and EXCEL trials, with seemingly conflicting results, brought into question whether percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) is better for low-risk patients with left main coronary artery stenosis (LMCAS). This review appraises the methods and results of NOBLE and EXCEL, contextualizes them within the literature, and determines how they may affect clinical practice. RECENT FINDINGS: We appraised the trials and describe differences in methodology and results. NOBLE recruited primarily isolated LMCAS, and found that CABG was superior to PCI. EXCEL's population included patients LMCAS in the context of multivessel CAD, and found PCI and CABG were comparable. Both trials enrolled young patients with few comorbidities, and there was more protocol-mandated consistency in the procedural techniques and medical therapy of patients receiving PCI. SUMMARY: The generalizability of these trials is limited by the use of young, healthy patients at highly skilled centres that rarely reflect typical clinical practice. If these studies are to maintain relevance, trialists must address the lack of protocolization of surgical interventions and inconsistent medical therapies. Unfortunately, the limitations of NOBLE and EXCEL mean that we are no closer to answering the question of what is the optimal treatment for patients with LMCAS.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".