What do we know about the pharmacotheraputic management of insomnia in cannabis withdrawal: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it