Primary care management of alcohol use disorder and at-risk drinking: Part 2: counsel, prescribe, connect.
Bibliographic record
Abstract
OBJECTIVE: To provide primary care physicians with evidence-based information and advice on the management of at-risk drinking and alcohol use disorder (AUD). SOURCES OF INFORMATION: We conducted a nonsystematic literature review using search terms that included primary care; screening, interventions, management, and treatment; and at-risk drinking, alcohol use disorders, alcohol dependence, and alcohol abuse; as well as specific medical and counseling interventions of relevance to primary care. MAIN MESSAGE: For their patients with at-risk drinking and AUD, physicians should counsel and, when indicated (ie, in patients with moderate or severe AUD), prescribe and connect. Counsel: Offer all patients with at-risk drinking a brief counseling session and follow-up. Offer all patients with AUD counseling sessions and ongoing (frequent and regular) follow-up. Prescribe: Offer medications (disulfiram, naltrexone, acamprosate) to all patients with moderate or severe AUD. Connect: Encourage patients with AUD to attend counseling, day or residential treatment programs, and support groups. If indicated, refer patients to an addiction medicine physician, concurrent mental health and addiction services, or specialized trauma therapy. CONCLUSION: Family physicians can effectively manage patients with at-risk drinking and AUD.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".