Predictors of Alcohol Relapse Following Liver Transplantation for Alcohol-Induced Liver Failure. Consideration of “A–D” Selection Criteria
Bibliographic record
Abstract
Demonstrated abstinence from alcohol for over six months and successful completion of a formal alcohol addictions program are two commonly employed criteria for determining whether an alcoholic patient with liver failure should proceed to liver transplantation. In this systematic review of the medical literature, we review the justification for these criteria and consider other variables that have also been reported to be of predictive value. While abstinence from alcohol for over six months is supported by the medical literature, data are more limited regarding the value of formal alcohol addictions program as selection criteria for proceeding towards liver transplantation. Positive family histories of alcoholism, co-inhabitants drinking alcohol in the presence of the patient and concurrent drug dependencies are more robust predictor variables of post-transplant recidivism. Based on the findings of this review, we propose a simple A-D transplantation selection criteria wherein "A" refers to demonstrated abstention from alcohol for over six months, "B" biology (a negative family history for alcoholism), "C", co-inhabitants not consuming alcohol in the presence of the patient; and "D", no concurrent drug dependency.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".