Donor BMI >30 Is Not a Contraindication for Live Liver Donation
Bibliographic record
Abstract
The increased prevalence of obesity worldwide threatens the pool of living liver donors. Although the negative effects of graft steatosis on liver donation and transplantation are well known, the impact of obesity in the absence of hepatic steatosis on outcome of living donor liver transplantation (LDLT) is unknown. Consequently, we compared the outcome of LDLT using donors with BMI <30 versus donors with BMI ≥30. Between April 2000 and May 2014, 105 patients received a right-lobe liver graft from donors with BMI ≥30, whereas 364 recipients were transplanted with grafts from donors with BMI <30. Liver steatosis >10% was excluded in all donors with BMI >30 by imaging and liver biopsies. None of the donors had any other comorbidity. Donors with BMI <30 versus ≥30 had similar postoperative complication rates (Dindo-Clavien ≥3b: 2% vs. 3%; p = 0.71) and lengths of hospital stay (6 vs. 6 days; p = 0.13). Recipient graft function, assessed by posttransplant peak serum bilirubin and international normalized ratio was identical. Furthermore, no difference was observed in recipient complication rates (Dindo-Clavien ≥3b: 25% vs. 20%; p = 0.3) or lengths of hospital stay between groups. We concluded that donors with BMI ≥30, in the absence of graft steatosis, are not contraindicated for LDLT.
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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 | 0.003 |
| 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) | 0.006 | 0.001 |
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".