The use of cannabinoids for sleep: A critical review on clinical trials.
Bibliographic record
Abstract
Cannabis and its pharmacologically active constituents, phytocannabinoids, have long been reported to have multiple medicinal benefits. One association often reported by users is sedation and subjective improvements in sleep. To further examine this association, we conducted a critical review of clinical studies examining the effects of cannabinoids on subjective and objective measures of sleep. PubMED, Web of Science, and Google Scholar were searched using terms and synonyms related to cannabinoids and sleep. Articles chosen included randomized controlled trials and open label studies. The Cochrane risk of bias tool was used to assess the quality of trials that compared cannabinoids with control interventions. The current literature focuses mostly on the use of tetrahydrocannabinol (THC) and/or cannabidiol (CBD) in the treatment of chronic health conditions such as multiple sclerosis, posttraumatic stress disorder (PTSD), and chronic pain. Sleep is often a secondary, rather than primary outcome in these studies. Many of the reviewed studies suggested that cannabinoids could improve sleep quality, decrease sleep disturbances, and decrease sleep onset latency. While many of the studies did show a positive effect on sleep, there are many limiting factors such as small sample sizes, examining sleep as a secondary outcome in the context of another illness, and relatively few studies using validated subjective or objective measurements. This review also identified several questions that should be addressed in future research. These questions include further elucidation of the dichotomy between the effects of THC and CBD, as well as identifying any long-term adverse effects of medicinal cannabinoid use. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".