Outcomes of Highly Selected Live Donors With a Future Liver Remnant Less Than or Equal to 30%: A Matched Cohort Study
Bibliographic record
Abstract
BACKGROUND: The main concern with live donor liver transplantation (LDLT) is the risk to the donor. Given the potential risk of liver insufficiency, most centers will only accept candidates with future liver remnants (FLR) >30%. We aimed to compare postoperative outcomes of donors who underwent LDLT with FLR ≤30% and >30%. METHODS: Adults who underwent right hepatectomy for LDLT between 2000 and 2018 were analyzed. Remnant liver volumes were estimated using hepatic volumetry. To adjust for between-group differences, donors with FLR ≤30% and >30% were matched 1:2 based on baseline characteristics. Postoperative complications including liver dysfunction were compared between the groups. RESULTS: A total of 604 live donors were identified, 28 (4.6%) of whom had a FLR ≤30%. Twenty-eight cases were successfully matched with 56 controls; the matched cohorts were mostly similar in terms of donor and graft characteristics. The calculated median FLR was 29.8 (range, 28.0-30.0) and 35.2 (range, 30.1-68.1) in each respective group. Median follow-up was 36.5 mo (interquartile range, 11.8-66.1). Postoperative outcomes were similar between groups. No difference was observed in overall complication rates (FLR ≤30%: 32.1% versus FLR >30%: 28.6%; odds ratio [OR], 1.22; 95% confidence interval [CI], 0.46-3.27) or major complication rates (FLR ≤30%: 14.3% versus FLR >30%: 14.3%; OR, 1.17; 95% CI, 0.33-4.10). Posthepatectomy liver failure was rare, and no difference was observed (FLR ≤30%: 3.6% versus FLR >30%: 3.6%; OR, 1.09; 95% CI, 0.11-11.1). CONCLUSION: A calculated FLR between 28% and 30% on its own should not represent a formal contraindication for live donation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".