Medical cannabis and cannabinoids for impaired sleep: a systematic review and meta-analysis of randomized clinical trials
Bibliographic record
Abstract
STUDY OBJECTIVES: We conducted a systematic review to explore the effectiveness of medical cannabis for impaired sleep. METHODS: We searched MEDLINE, EMBASE, CENTRAL, and PsychINFO to January 2021 for randomized trials of medical cannabis or cannabinoids for impaired sleep vs. any non-cannabis control. When possible, we pooled effect estimates for all patient-important sleep-related outcomes and used the GRADE approach to appraise the certainty of evidence. RESULTS: Thirty-nine trials (5100 patients) were eligible for review, of which 38 evaluated oral cannabinoids and 1 administered inhaled cannabis. The median follow-up was 35 days, and most trials (33 of 39) enrolled patients living with chronic cancer or noncancer chronic pain. Among patients with chronic pain, moderate certainty evidence found that medical cannabis probably results in a small improvement in sleep quality versus placebo (modeled risk difference [RD] for achieving the minimally important difference [MID], 8% [95% CI, 3 to 12]). Moderate to high certainty evidence shows that medical cannabis vs. placebo results in a small improvement in sleep disturbance for chronic non-cancer pain (modeled RD for achieving the MID, 19% [95% CI, 11 to 28]) and a very small improvement in sleep disturbance for chronic cancer pain (weighted mean difference of -0.19 cm [95%CI, -0.36 to -0.03 cm]; interaction p = .03). Moderate to high certainty evidence shows medical cannabis, versus placebo, results in a substantial increase in the risk of dizziness (RD 29% [95%CI, 16 to 50], for trials with ≥3 months follow-up), and a small increase in the risk of somnolence, dry mouth, fatigue, and nausea (RDs ranged from 6% to 10%). CONCLUSION: Medical cannabis and cannabinoids may improve impaired sleep among people living with chronic pain, but the magnitude of benefit is likely small.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.028 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".