Is obesity a predictor of mortality, morbidity and readmission after cardiac surgery?
Bibliographic record
Abstract
INTRODUCTION: Obesity has been described as a risk factor for the development of coronary artery disease, but it has not been determined whether obesity is associated with adverse outcomes after cardiac surgery. Therefore, we analyzed a large cohort of patients who had undergone cardiac surgery to determine whether obesity is a predictor of mortality, morbidity or early readmission to hospital. METHODS: At the London Health Sciences Centre, an academic tertiary care centre, we prospectively entered data from the cardiac surgical database from July 1999 to April 2002. We collected data on 1310 consecutive, unselected patients who underwent cardiac surgery during that time. We assessed the degree of obesity using the body mass index (BMI), and we prospectively documented the occurrence of 10 major complications after surgery. They included stroke, reoperation for bleeding, life-threatening cardiac arrest or arrhythmia, new renal failure requiring dialysis, septicemia, mediastinitis, sternal dehiscence, respiratory failure, postoperative myocardial infarction and low cardiac output necessitating intra-aortic balloon pump use. Univariable and multivariable analyses were conducted to determine the factors associated with and predictive of postoperative death and major complications. RESULTS: An increased BMI did not increase the risk of early postoperative death. Furthermore, increased BMI was not a predictor of a patient experiencing any of the major complications, except sternal dehiscence. An increased BMI was associated with a higher likelihood of readmission to hospital within 30 days of discharge. CONCLUSION: Obesity was not associated with adverse outcomes after cardiac operations, aside from the increased risks of sternal dehiscence and early hospital readmission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".