Revascularization strategies for left main coronary artery disease: current perspectives
Bibliographic record
Abstract
PURPOSE OF REVIEW: Left main coronary artery disease (LMCAD) represents a high-risk subset of coronary artery disease with significant morbidity and mortality if not treated in a timely manner. In this review, we survey the contemporary evidence on the management of LMCAD, highlight advances, and provide in-depth review of data comparing surgical and percutaneous approaches. RECENT FINDINGS: LMCAD represents a heterogeneous condition and management should be guided by key clinical and anatomic factors. In recent years, there has been a wealth of published prospective data including results of the EXCEL and NOBLE trials. Coronary artery bypass graft (CABG), remains the gold standard for optimal long-term outcomes and the greatest benefit seen in patients with higher anatomic complexity and longer life expectancy. Percutaneous coronary intervention (PCI) offers a less-invasive approach with rapid recovery. PCI is optimal in situations when surgery cannot be offered in a timely manner due to hemodynamic instability, for high-risk surgical patients, or those with limited life expectancy, if LMCAD is anatomically simple. As a result of continued technological and procedural improvements in both PCI and CABG, cardiovascular specialists possess a growing armamentarium of approaches to treat LMCAD. Thus, center specialization and use of a heart team approach are increasingly vital, though barriers remain. SUMMARY: Emerging evidence continues to support CABG as the gold standard for achieving optimal long-term outcomes in patients with LMCAD. PCI offers a more expeditious approach with rapid recovery and is a safe and effective alternative in appropriately selected candidates.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".