Clinical management of cannabis withdrawal
Bibliographic record
Abstract
BACKGROUND AND AIMS: -tetrahydrocannabinol (THC). This review describes the diagnosis, prevalence, course and management of cannabis withdrawal and highlights opportunities for future clinical research. METHODS: Narrative review of literature. RESULTS: Symptom onset typically occurs 24-48 hours after cessation and most symptoms generally peak at days 2-6, with some symptoms lasting up to 3 weeks or more in heavy cannabis users. The most common features of cannabis withdrawal are anxiety, irritability, anger or aggression, disturbed sleep/dreaming, depressed mood and loss of appetite. Less common physical symptoms include chills, headaches, physical tension, sweating and stomach pain. Despite limited empirical evidence, supportive counselling and psychoeducation are the first-line approaches in the management of cannabis withdrawal. There are no medications currently approved specifically for medically assisted withdrawal (MAW). Medications have been used to manage short-term symptoms (e.g. anxiety, sleep, nausea). A number of promising pharmacological agents have been examined in controlled trials, but these have been underpowered and positive findings not reliably replicated. Some (e.g. cannabis agonists) are used 'off-label' in clinical practice. Inpatient admission for MAW may be clinically indicated for patients who have significant comorbid mental health disorders and polysubstance use to avoid severe complications. CONCLUSIONS: The clinical significance of cannabis withdrawal is that its symptoms may precipitate relapse to cannabis use. Complicated withdrawal may occur in people with concurrent mental health and polysubstance use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".