People with Alcohol Use Disorders in Specialized Care in Eight Different European Countries
Bibliographic record
Abstract
AIM: To provide a description of patients receiving alcohol treatment in eight different European countries, including the level of comorbidities and functional limitations. METHODS: Drinking behaviours, DSM-IV alcohol use disorder (AUD), mental and somatic comorbidities, disability and health services utilization of 1767 patients from various specialized treatment settings were assessed as representative for regions of eight European countries. Severity of alcohol dependence (AD) in terms of drinking level was compared with a large representative US sample. RESULTS: Patients in specialized care for AUDs showed high levels of consumption [average level of daily ethanol intake: 141.1 g, standard deviation (SD): 116.0 g], comorbidity [e.g. liver problems: 19.6%, 95% confidence interval (CI): 17.5-21.6%; depression: 43.2%, 95% CI: 40.7-45.8%; anxiety: 50.3%, 95% CI: 47.8-52.9%], disability and health services utilization (average number of nights spent in hospital(s) during the last 6 months: 8.8, SD: 19.5 nights). Severity of AD was similar to the US sample, but European men consumed on average more alcohol daily. CONCLUSIONS: High levels of consumption, somatic and mental comorbidities, disability and functional losses were found in this representative treatment sample, indicating that treatment was initiated only at severe stages of AUDs. Earlier initiation of treatment could help avoid some of the health and social burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".