Restricting Liver Transplant Recipients to Younger Donors Does Not Increase the Wait-List Time or the Dropout Rate: The Hepatitis C Experience
Bibliographic record
Abstract
Older donor age is associated with lower graft and patient survival among all recipients of liver transplantation (LT). Among patients with hepatitis C virus (HCV), donor age is one of the strongest predictors of fibrosis severity and graft loss. We evaluated the implementation of a donor age restriction policy for LT patients with HCV at a single center and the effects that this policy had on wait-list (WL) and post-LT outcomes for HCV and non-HCV patients. This was a cohort study of 2388 WL patients and 1015 LT recipients between March 2002 and January 2013 and reflected 3 different eras of donor age policies. With the donor age restriction, the median donor age was reduced in LT recipients with HCV versus LT recipients without HCV (30 versus 48 years, P < 0.001) without differences in the WL time (10.6 versus 8.0 months, P = 0.23). According to a competing risks regression, those with HCV and those without HCV had lower subhazard ratios (SHRs) of dropout or death on the WL during the donor age restriction era versus the era without donor age restriction [SHR = 0.68 (P < 0.01) and SHR = 0.64 (P = 0.01), respectively]. No differences were seen in early post-LT survival for patients with or without HCV between eras (P = 0.7 and P = 0.88, respectively). In conclusion, we show that donor age restriction for HCV results in a lower donor age for HCV recipients without obvious adverse WL consequences. Although additional studies are needed, our results demonstrate the feasibility of donor age restriction for LT recipients with HCV, and such information may be relevant to programs with limited access to new antiviral therapies for which modifying the risk of severe disease remains of paramount importance.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".