Assessment and management of alcohol use disorders
Bibliographic record
Abstract
Alcohol can impact on both the incidence and the course of many health conditions, and nearly 6% of all global deaths in 2012 were estimated to be attributable to its consumption.1 A quarter of the UK adult population drinks alcohol in a way that is potentially or actually harmful to health. 2 Between 2002 and 2012 in England the number of episodes where an alcohol related disease, injury, or condition was the primary reason for hospital admission or a secondary diagnosis doubled.3 Despite the large numbers of people drinking alcohol at higher risk levels, a relatively low number access treatment.4 Possible causes for this include missed opportunities to identify problems, limited access to specialist services, and underdeveloped care pathways.International studies have shown that more than 20% of patients presenting to primary care are higher risk or dependent drinkers, 5 yet the problem of alcohol is inadequately addressed.This review focuses on practical aspects of the assessment and treatment of alcohol use disorders from the perspective of the non-specialist hospital doctor or general practitioner.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".