Cannabis dosing and administration for sleep: a systematic review
Bibliographic record
Abstract
STUDY OBJECTIVES: As cannabis is increasingly used to treat sleep disorders, we performed a systematic review to examine the effects of cannabis on sleep and to guide cannabis prescribers in their recommendations to patients, specifically focusing on dosing. METHODS: We searched EMBASE, Medline, and Web of Science and identified 4550 studies for screening. Five hundred sixty-eight studies were selected for full-text review and 31 were included for analysis. Study results were considered positive based on improvements in sleep architecture or subjective sleep quality. Bias in randomized controlled trials was assessed using Cochrane Risk of Bias tool 2.0. RESULTS: Sleep improvements were seen in 7 out of 19 randomized studies and in 7 out of 12 uncontrolled trials. There were no significant differences between the effects of tetrahydrocannabinol and cannabidiol. Cannabis showed most promise at improving sleep in patients with pain-related disorders, as compared to those with neurologic, psychiatric, or sleep disorders, and showed no significant effects on healthy participants' sleep. While subjective improvements in sleep quality were often observed, diagnostic testing showed no improvements in sleep architecture. Adverse events included headaches, sedation, and dizziness, and occurred more frequently at higher doses, though no serious adverse events were observed. CONCLUSION: High-quality evidence to support cannabis use for sleep remains limited. Heterogeneity in cannabis types, doses, timing of administration, and sleep outcome measures limit the ability to make specific dosing recommendations.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".