Association between overweight and obesity with coronary artery bypass graft failure: an individual patient data analysis of clinical trials
Bibliographic record
Abstract
OBJECTIVES: The association between obesity and graft failure after coronary artery bypass grafting has not been previously investigated. METHODS: We pooled individual patient data from randomized clinical trials with systematic postoperative coronary imaging to evaluate the association between obesity and graft failure at the individual graft and patient levels. Penalized cubic regression splines and mixed-effects multivariable logistic regression models were performed. RESULTS: Six trials comprising 3928 patients and 12 048 grafts were included. The median time to imaging was 1.03 (interquartile range 1.00-1.09) years. By body mass index (BMI) category, 800 (20.4%) patients were normal weight (BMI 18.5-24.9), 1668 (42.5%) were overweight (BMI 25-29.9), 983 (25.0%) were obesity class 1 (BMI 30-34.9), 344 (8.8%) were obesity class 2 (BMI 35-39.9) and 116 (2.9%) were obesity class 3 (BMI 40+). As a continuous variable, BMI was associated with reduced graft failure [adjusted odds ratio (aOR) 0.98 (95% confidence interval (CI) 0.97-0.99)] at the individual graft level. Compared to normal weight patients, graft failure at the individual graft level was reduced in overweight [aOR 0.79 (95% CI 0.64-0.96)], obesity class 1 [aOR 0.81 (95% CI 0.64-1.01)] and obesity class 2 [aOR 0.61 (95% CI 0.45-0.83)] patients, but not different compared to obesity class 3 [aOR 0.94 (95% CI 0.62-1.42)] patients. Findings were similar, but did not reach significance, at the patient level. CONCLUSIONS: In a pooled individual patient data analysis of randomized clinical trials, BMI and obesity appear to be associated with reduced graft failure at 1 year after coronary artery bypass grafting.
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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 | 0.095 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".