Grayken lessons: between a rock and a hard place? A 37-year-old man with acute liver injury while enrolled in a managed alcohol program for severe alcohol use disorder
Bibliographic record
Abstract
Managed alcohol programs aim to reduce health and social harms associated with severe alcohol use disorder. Here, we describe a young man with severe alcohol use disorder enrolled in a managed alcohol program, who was admitted to hospital with acute liver injury. Fearing that alcohol was contributing, the inpatient care team discontinued the managed alcohol dose in hospital. He was ultimately diagnosed with cephalexin-induced liver injury. After consideration of risks, benefits, and alternative options, the patient and care team jointly decided to restart managed alcohol after hospital discharge. With this case, we describe managed alcohol programs and summarize the emerging evidence-base, including eligibility criteria and outcome measures; we explore clinical and ethical dilemmas in caring for patients with liver disease within managed alcohol programs; and we emphasize principles of harm reduction and patient-centered care when establishing treatment plans for patients with severe alcohol use disorder and unstable housing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".