Alcohol use while on the liver transplant waiting list: A single-center experience
Bibliographic record
Abstract
Alcoholic liver disease (ALD) is a leading indication for liver transplantation. Our center has randomly checked blood alcohol levels (BALs) in ALD patients on the waiting list since 2004. We aimed to identify the incidence and predictors of inactivation on the transplant list due to alcohol use and to determine the utility of BAL-screening in this process. We conducted a retrospective review of patients with ALD listed for liver transplantation with at least 3 months of postlisting follow-up. Alcohol use while on the transplant list was defined as a positive BAL, an admission of alcohol use, or refusal to perform screening within 12 hours of request. Cox proportional hazards regression was used to estimate risk ratios (RRs). Of 134 patients meeting eligibility criteria, 78% were male, and mean age was 52 years. Alcohol use was documented in 23 patients (17%). Of these, 12 refused to have a random screen, 8 had detectable serum ethanol levels, and 3 had self-reported alcohol use. On multivariable analysis, a higher number of random BAL-checks [RR = 0.63(0.52, 0.76), P = 0.001] and a longer duration of prelisting abstinence [RR = 0.88(0.83, 0.94), P = 0.001] independently reduced the risk of alcohol use by patients while on the waiting list. None of the patients with >24 months of prelisting abstinence had a positive screen. In conclusion, this study supports random BAL-screening before transplantation and reinforces the importance of abstinence duration as a predictor of relapse. For patients with <24 months of prelisting abstinence, our center will increase the frequency of random BAL screening and increase the rehabilitation requirements to include an intensive 3-week rehabilitation program. We hope that these measures will reduce the rate of relapse to alcohol use post-transplantation.
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) | 0.000 | 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) | 0.000 | 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".