Primary care management of alcohol use disorder and at-risk drinking: Part 1: screening and assessment.
Bibliographic record
Abstract
OBJECTIVE: To provide primary care physicians with evidence-based information and advice on the screening and assessment of at-risk drinking and alcohol use disorder (AUD). A companion article outlines the management of at-risk drinking and AUD. SOURCES OF INFORMATION: We conducted a nonsystematic literature review, using search terms on primary care, AUD, alcohol dependence, alcohol abuse, alcohol misuse, unhealthy drinking, and primary care screening, identification, and assessment. MAIN MESSAGE: Family physicians should screen all patients at least yearly for unhealthy drinking with a validated screening test. Screen patients who present with medical or psychosocial problems that might be related to alcohol use. Determine if patients who have positive screening results are at-risk drinkers or have AUD. If patients have AUD, categorize it as mild, moderate, or severe using the Diagnostic and Statistical Manual of Mental Disorders, 5th edition, criteria. Share this diagnosis with the patient and offer assistance. Do a further assessment for patients with AUD. Screen for other substance use, concurrent disorders, and trauma. Determine whether there is a need to report to child protection services or the Ministry of Transportation. Determine the need for medical management of alcohol withdrawal. Conduct a brief physical examination and order laboratory tests to assess complete blood count and liver transaminase levels, including γ-glutamyl transpeptidase. CONCLUSION: Primary care is well suited to screening and assessment of alcohol misuse.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".