Coronary Artery Bypass Grafting in Patients with Left Ventricular Dysfunction
Bibliographic record
Abstract
BACKGROUND: Coronary artery bypass grafting surgery (CABG) remains a challenge for patients with coronary artery disease and left ventricular (LV) dysfunction. The aim of this study was to evaluate the result of CABG in patients with LV dysfunction. METHODS: Medical records of 1,847 patients who underwent primary, isolated CABG at Taipei Veterans General Hospital from January 1, 1991 to December 31, 2002, were reviewed. The mortality rate associated with clinical and operative variables was compared between patients with LV ejection fraction (LVEF) > or = 35% and patients with LVEF < 35%. RESULTS: Patients with LVEF < 35% had more episodes of myocardial infarction (57.5% vs 28.9%, p < 0.001) and history of congestive heart failure (18.1% vs 3.2%, p < 0.001), higher New York Heart Association (NYHA) class, and higher angina class. Longer cardiopulmonary bypass time (147 +/- 44 minutes vs 137 +/- 40 minutes, p < 0.001) but fewer left internal mammary artery (LIMA) grafts (46.8% vs 65.7%, p < 0.001) were used in patients with LVEF < 35%. Patients with LVEF < 35% had significantly higher hospital mortality (6.6% vs 2.2%, p < 0.001), higher major morbidity (23.3% vs 16.1%, p < 0.01), and longer hospital stay (25 +/- 23 days vs 21 +/- 16 days, p < 0.01). CONCLUSION: Although patients with LV dysfunction had higher mortality and morbidity, CABG could be done in these high-risk patients with acceptable results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".