Outcome of coronary artery bypass graft surgery in patients with low ejection fraction
Bibliographic record
Abstract
Background: Patients with low ejection fraction (EF) are at high risk for postoperative complication and mortality. Our aim was to assess the effect of low EF on clinical outcome after surgery. Objective: The present study evaluates our experience with coronary artery bypass grafting in patients with low EF. Materials and Methods: We analyzed the data of 35 patients with EF <35%. All participants were assessed preoperatively with respect to patient characteristics, risk factors, and preoperative two dimension echocardiography (2D ECHO). Depending on findings of 2D ECHO patients were divided into three groups: in Group 1, we included patients with EF 30%–35%, Group 2 comprised patients with EF of 25%–30%, and Group 3 consisted of patients with EF <25%. Patients were operated by off-pump coronary artery bypass grafting; only three patients were operated by on-pump beating heart surgery. We noted a mean number of grafts required, use of intra-aortic balloon counterpulsation (intra-aortic balloon pump) intraoperatively and postoperatively, postoperative complication, mortality, mean hospital stay, postoperative improvement in EF, and postoperative control of angina and symptomatic improvement. Results: Hospital mortality rate in present series was 11%. Mean grafts were 3.02 per patient. Fourteen (40%) patient had a postoperative complication. EF improved in 78% of patients. Canadian Cardiovascular Society Angina class improved in 42% of patients. Conclusion: In patients with coronary artery disease and low EF, CABG can be performed safely, and improvement in left ventricular function can be achieved with this procedure improving the quality of life.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".