Comparable graft and patient survival in lean and obese liver transplant recipients
Bibliographic record
Abstract
Obesity is among the great health problems facing Americans today. More than 32% of the US population is considered obese on the basis of a body mass index (BMI) exceeding 30 kg/m(2) . Obesity increases the risk for numerous perioperative complications, but how obesity affects the outcome of liver transplantation remains unclear. We compared graft/patient survival after orthotopic liver transplantation performed at the Cleveland Clinic between April 2005 and June 2011 in 2 groups: obese patients with a BMI ≥ 38 kg/m(2) and lean patients with a BMI between 20 and 26 kg/m(2) . We included 47 obese patients and 183 lean patients, whose demographics and baseline characteristics were well balanced after weighting with the inverse propensity score. After we controlled for observed confounding, no significant differences were observed in graft/patient survival between obese and lean patients (P = 0.30). The estimated hazard ratio for obese patients to experience graft failure or death was 1.19 [95% confidence interval (CI) = 0.85-1.67]. There were 134 patients who had follow-up for more than 3 years, and they included 27 obese patients and 107 lean patients. Within this subset, the odds of having metabolic syndrome were significantly greater for obese patients (46%) versus lean patients (21%; odds ratio = 4.76, 99.5% CI = 1.66-13.7, P < 0.001). However, no significant association between obesity and any other long-term adverse outcomes was found. In conclusion, this study shows that transplant outcomes were comparable for lean and obese recipients. We thus recommend that even morbid obesity per se should not exclude patients from consideration for transplantation.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".