Cannabis and sleep architecture: A systematic review and meta-analysis
Bibliographic record
Abstract
Cannabis use for sleep is increasingly prevalent, yet its effects on sleep architecture remain unclear. This systematic review and meta-analysis examined polysomnographic evidence on cannabis' impact on sleep parameters. Eighteen studies were identified, with nine suitable for meta-analysis. Findings indicate that cannabis administration does not consistently alter sleep duration, latency, wake time, efficiency, or sleep staging. While early studies suggested reductions in rapid eye movement sleep, these were primarily based on small-scale trials with high tetrahydrocannabinol doses and significant methodological limitations. More recent studies using larger samples and lower therapeutic doses of tetrahydrocannabinol have reported mixed (and often no) evidence of rapid eye movement (REM) suppression, and the evidence base remains very limited. However, withdrawal from active cannabis use was consistently associated with sleep disturbances, including reduced total sleeping times and prolonged sleep onset latency, as well as REM rebounds. Variability in study outcomes highlights the influence of factors such as dosage, cannabinoid composition, prior cannabis use, and health conditions. Further research using standardised protocols and larger samples is needed to clarify the relationship between cannabis and sleep architecture and to address the discrepancies between subjective sleep improvements and objective sleep metrics.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.027 | 0.003 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".