Mid-Term Outcome and Angiographic Follow-Up of Endarterectomy of the Left Anterior Descending Artery in Patients Undergoing Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND AND AIM: With the advancement of percutaneous coronary interventions (PCIs), more patients with diffuse coronary artery disease are referred for coronary artery bypass graft (CABG) surgery. The use of coronary endarterectomy may be useful in such cases. We reviewed our experience with left anterior descending artery endarterectomy as an adjunct to conventional CABG. METHODS: Between June 2005 and 2011, 58 consecutive patients underwent left anterior descending artery endarterectomy as an adjunct to CABG. These were matched to 58 cases based on age, gender, and Parsonnet score. All data were collected prospectively in a departmental database. Postoperative complications and in-hospital mortality were analyzed. Survival curves were produced. RESULTS: There was one death in the endarterectomy group (1.7%) from liver failure. There was no significant difference in postoperative complications (especially perioperative myocardial infarction) between the two groups with similar hospital mortality. Computed tomography (CT) angiography was performed in 24 patients with endarterectomy (41%), which showed 100% patency of the left internal mammary artery graft to the left anterior descending artery. Survival and freedom from intervention at a mean follow-up of 4.2 years were similar. CONCLUSIONS: In patients with diffuse disease, the use of endarterectomy is a safe technique with no increase in short-term morbidity or mortality. Mid-term results are similar to nonendarterectomized patients. This technique is useful in patients with diffuse coronary artery disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".