What do we know about the pharmacotheraputic management of insomnia in cannabis withdrawal: A systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sleep disturbance is one of the hallmarks of cannabis withdrawal. Studies have indicated that treatment of this key symptom may facilitate abstinence. In the present paper we aim to provide a systematic review of the extant literature on pharmacological management of sleep disturbance associated with cannabis withdrawal. METHOD: We conducted a systematic literature search across five electronic databases including PubMed, Psycinfo, MEDLINE, Cochrane review and Embase. Human studies using a pharmacological treatment for sleep disturbances associated with cannabis withdrawal were included. Review articles, case-series, open trials, posters, and editorials were excluded. RESULTS: Seventeen publications, involving 562 participants, were included in this review. Major limitations involved small sample size, high dropout rate, methodological limitations, and heterogeneity of participants. Most of the studies were at high risk of bias, further downgrading the level of evidence. A meta-analysis was not performed due to lack of quantitative data, marked heterogeneity and low quality of the included studies. CONCLUSION: There is not sufficient evidence for any of the reviewed treatment options. Methodological limitations in a majority of the studies rendered their findings preliminary. Of the twelve investigated pharmacological agents, Gabapentin, Lofexidine, Mirtazapine, Quetiapine, and Zolpidem showed some primary benefits for treatment of sleep difficulties associated with cannabis withdrawal; however, future prospective studies are required to confirm such results. SCIENTIFIC SIGNIFICANCE: This review examines the current evidence for potential pharmacological options for treatment of cannabis withdrawal and associated sleep disturbance. It furthers our knowledge and provides groundwork for future research. (Am J Addict 2018;27:453-464).
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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".