Predicting Pre-transplant Abstinence in Patients with Alcohol-Induced Liver Disease
Bibliographic record
Abstract
BACKGROUND: Some patients with alcohol-induced liver failure will succumb to their disease prior to demonstrating compliance with the six months abstinence rule for liver transplantation. PURPOSE: The purpose of this study was to determine whether a patient's self-reported, longest period of abstinence predicts subsequent abstinence. METHODS: Adult patients (n=63) with alcohol-induced liver disease were asked to recall their longest period of abstinence prior to their initial hepatology visit. Compliance with instructions to remain abstinent was then documented. RESULTS: Nineteen patients (30%) achieved abstinence and 44 (70%) relapsed within six months of seeing their hepatologist. Relapses were more common in patients who self-reported previous periods of abstinence exceeding six months (19/44, 43%) compared with 2/19 (11%) in those with periods of less than six months (p=0.01). Serum albumin levels were lower in relapsers but other tests of liver function (bilirubin level and international ratio of prothrombin time) and predictors of post-transplant recidivism did not associate with relapses. On multivariate analysis, self-reported abstinence (OR: 0.11, 95% CI: 0.02-0.57, p=0.008) and serum albumin levels (regression coefficient 0.113, p=0.02) predicted relapses. CONCLUSIONS: A self-reported period of abstinence in excess of six months was associated with an increased risk of subsequent relapse following a hepatologist's instructions to remain abstinent. These counter-intuitive findings should be confirmed by larger, prospective studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".