Impact of body mass index on outcomes of 48281 patients undergoing first time cadaveric liver transplantation
Bibliographic record
Abstract
AIM: To investigate possible disparities in perioperative morbidity and mortality among different body mass index (BMI) groups and to simulate the impact that these differences might have had on the cohort of patients undergoing cadaveric liver transplantation (LT). METHODS: All adult recipients undergoing first time LT for benign conditions and receiving a whole graft from brain-dead donors were selected from the united network of organ sharing registry. From January 1994 to June 2013, 48281 patients satisfied the inclusion criteria and were stratified by their BMI. The hypothesis that abnormal BMIs were independent predictors of inferior outcomes was tested with univariate and multivariate regression analyses. RESULTS: In comparison to normal weight recipients, underweight and morbidly obese recipients had increased 90-d mortality (adjusted OR = 1.737; 95%CI: 1.185-2.548, P = 0.005) (adjusted OR = 1.956; 95%CI: 1.473-2.597, P = 0.000) respectively and inferior patients' survivals (adjusted HR = 1.265; 95%CI: 1.096-1.461, P = 0.000) (adjusted HR = 1.157; 95%CI: 1.031-1.299, P = 0.013) respectively. Overall, patients' 5-year survival were 73.9% for normal-weight, 71.1% for underweight, 74.0% for overweight, 74.4% for class I obese, 75.0% for class II obese and 71.5% for class III obese recipients. Analysis of hypothetical exclusion of underweight and morbidly obese patients from the pool of potential LT candidates would have improved the overall survival of the entire cohort by 2.7% (95%CI: 2.5%-3.6%). CONCLUSION: Selected morbidly obese patients undergoing LT for benign conditions had 5-year survival rates clinically comparable to normal weight recipients. Impact analysis showed that exclusion of high-risk recipients (underweight and morbid obese patients) would not significantly improve the overall survival of the entire cohort of patients requiring LT.
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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 | 0.000 | 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 | 0.001 |
| 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".