Utilization of biomarkers for alcohol use in candidates for liver transplantation with alcohol-associated liver disease
Bibliographic record
Abstract
Although a set period of abstinence is no longer a requirement for transplant consideration in many liver transplantation programs, it is imperative to use valid monitoring tools to detect ongoing alcohol use in candidates for transplant. Urinary ethyl glucuronide (EtG) is an objective measure of alcohol use. This single-center retrospective study aims to describe the psychosocial characteristics of patients with alcohol-associated liver disease (ALD) who provided positive EtG tests in the pretransplant phase. Data were collected between May 1, 2018, and November 30, 2021, for all patients with ALD referred to our transplantation program (n = 497). Psychosocial characteristics and transplant outcomes were recorded for all patients. Patients with a positive EtG test were compared to patients who did not have a positive EtG test. A backward logistic regression analysis was performed to assess the factors associated with a positive EtG test. Of the 497 patients evaluated, 40 (8%) provided a positive EtG test, including at the initial clinic visit (n = 20), during medical evaluation (n = 14), and while on the waitlist (n = 6). Severe alcohol use disorder ( p < 0.01), consuming <10 daily standard drinks ( p = 0.011), and longer duration of daily alcohol use ( p = 0.028) were significantly associated with a positive EtG test. Psychiatric comorbidity and previous treatment for alcohol use disorder were not significantly associated with positive tests. A minority of patients with ALD provided a positive urine EtG in the pretransplant phase. Alongside clinical interviews, biomarker testing is an objective tool to identify ongoing alcohol use in patients with ALD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".