Cannabis Use Disorders in Primary Care: Screening and Treatment Suggestions
Bibliographic record
Abstract
Objective: To review, through case-based learning, the screening for and treatment of cannabis use disorders in primary care. Harm reduction modalities as it pertains to cannabis use are also reviewed.Methods: PubMed was searched for studies on cannabis use disorders with a specific focus on screening and treatment modalities. The literature cited was reviewed for those relevant studies identified through PubMed.Results: Problematic cannabis use can be screened for with a single question. A positive screening question can lead to more intensive screening with a validated questionnaire. A high score on a validated questionnaire may lead to examination of the DSMV criteria. Withdrawal symptoms can be treated with synthetic cannabinoids. Maintenance may involve cannabinoids or other medications but the evidence is more mixed for this setting. Harm reduction techniques may be useful in those that are not interested in cutting down or in those that are willing to reduce but not stop cannabis consumption. Brief intervention may be effective in the primary care setting. There are patients who should not use cannabis.Conclusion: Primary care providers should screen for cannabis use disorders with a single question in those patients presenting with problems that could be related to substance use. More in-depth screening is triggered by a positive response. Withdrawal management, maintenance therapy and harm reduction are all within the scope of primary care providers and should be explored with the patients. If these interventions are unsuccessful, the patient can be referred to an addiction medicine specialist.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".