Availability of living donor optimizes timing of liver transplant in high-risk waitlisted cirrhosis patients
Bibliographic record
Abstract
Liver transplant (LT) candidates have become older and frailer, with growing Non-alcoholic steatohepatitis (NASH) and comorbid disease burden in recent years, predisposing them for poor waitlist outcomes.We aimed to evaluate the impact of access to living donor liver transplantation (LDLT) in waitlisted patients at highest risk of dropout.We reviewed all adult patients with decompensated cirrhosis listed for LT from November 2012 to December 2018.Patients with a potential living donor (pLD) available were identified.Survival analyses with Cox Proportional Hazards models and time to LT with Competing risk models were performed followed by prediction model development.Out of 860 patients who met inclusion criteria, 360 (41.8%) had a pLD identified and 496 (57.6%) underwent LT, out of which 170 (34.2%) were LDLT.The benefit of pLD was evident for all, but patients with moderate to severe frailty at listing (interaction p = 0.03), height <160 cm (interaction p = 0.03), and Model for end stage liver disease (MELD)-Na score <20 (interaction p < 0.0001) especially benefited.Our prediction model identified patients at highest risk of dropout while waiting for deceased donor and most benefiting of pLD (time-dependent area under the receiver operating characteristic curve 0.82).Access to LDLT in a transplant program can optimize the timing of transplant for the increasingly older, frail patient population with comorbidities who are at highest risk of dropout.
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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) | 0.001 | 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".