Outcome & Effect of Coronary Artery Bypass Graft Surgery on Left Ventricular Systolic Function: A Descriptive Study
Bibliographic record
Abstract
Background and Objectives: Patients with low ejection fraction (EF) are at high risk for postoperative complication and mortality. Our Aim: outcome & effect of coronary artery bypass graft surgery on left ventricular systolic function.Materials and Methods: This is a descriptive study.Between January 2017 to August 2019, 51 consecutive patients underwent isolated CABG at the Rajarajeshwari medical college and Hospital.Of these, 51 had echocardiographic assessment of LV function, 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.Appropriate investigations were done.Results: Hospital mortality rate in present series was 9.8%.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: We have shown that cardiac surgery provides long-term cardiac-death-free survival benefit in patients of all subsets of LV function, including poor and very poor LV function.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".