General Practitioners Recognizing Alcohol Dependence: A Large Cross-Sectional Study in 6 European Countries
Bibliographic record
Abstract
PURPOSE: Although alcohol dependence causes marked mortality and disease burden in Europe, the treatment rate is low. Primary care could play a key role in reducing alcohol-attributable harm by screening, brief interventions, and initiating or referral to treatment. This study investigates identification of alcohol dependence in European primary care settings. METHODS: Assessments from 13,003 general practitioners, and 9,098 interviews (8,476 joint number of interviewed patients with a physician's assessment) were collected in 6 European countries. Alcohol dependence, comorbidities, and health service utilization were assessed by the general practitioner and independently using the Composite International Diagnostic Interview (CIDI) and other structured interviews. Weighted regression analyses were used to compare the impact of influencing variables on both types of diagnoses. RESULTS: The rate of patients being identified as alcohol dependent by the CIDI or a general practitioner was about equally high, but there was not a lot of overlap between cases identified. Alcohol-dependent patients identified by a physician were older, had higher rates of physicial comorbidity (liver disease, hypertension), and were socially more marginalized, whereas average consumption of alcohol and mental comorbidity were equally high in both groups. CONCLUSION: General practitioners were able to identify alcohol dependence, but the cases they identified differed from cases identified using the CIDI. The role of the CIDI as the reference standard should be reexamined, as older alcohol-dependent patients with severe comorbidities seemed to be missed in this assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".