695 Cannabinoids and Sleep Health in Patients with Chronic Neuropathic Pain: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Introduction Neuropathic pain (NP) syndromes are debilitating conditions which can impact sleep health and overall quality of life significantly. Pharmacological treatment with cannabinoids has not been evaluated for its impact on sleep health. The objectives of this systematic review and meta-analysis were to determine the effect of cannabinoids on sleep quality, pain control, and patient impression of treatment efficacy. Methods We reviewed randomized controlled trials comparing synthetic and natural cannabinoids (CB) to placebo in patients with central and peripheral neuropathic pain syndromes. A systematic search of the standard literature databases was conducted, including randomized controlled trials evaluating the pharmacological treatment of NP syndromes using cannabinoids. Data on NRS pain scales, sleep quality, daytime somnolence, nausea, dizziness, and patient global impression of change (PGIC) scores were recorded. Meta-analysis using the random effects model was conducted where appropriate. Results Of the 3536 studies screened, a total of 8 randomized controlled trials including 1051 patients (placebo: 478 patients; CB: 573 patients) with neuropathic pain were included. Cannabinoids included in the studies were Sativex (GW-1000–02), Nabilone, and medical cannabis preparations with THC dose ranging from 1mg to 130mg per day. Pain scores were significantly reduced in the CB group (standardized difference in means (SDM) = -0.236, 95% CI=-0.375 to -0.100, p-value = 0.001) compared to placebo (Figure 1). Significant improvement in sleep quality (Figure 2) was also observed in the CB group (SMD 0.389, 95% CI, 0.233 to 0.546, p<0.013). Additionally, patients in the CB group were more likely to report improvement in PGIC scores (OR=2.3, 95% CI 1.37 to 3.9, p=0.002) compared to patients treated with placebo (Figure 3). Notably, CB-treated patients were more likely to experience daytime somnolence (OR=2.2, 95% CI 1.3 to 3.9, p=0.004), nausea (OR=1.7, 95% CI 1.1 to 2.5, p=0.02), and dizziness (OR=3.8, 95% CI 2.6 to 5.7, p<0.001). Conclusion Cannabinoids are useful agents for NP as evidenced by significant improvement in pain, sleep quality, and PGIC. With the advent of new agents and more refined cannabis derivatives, further research is needed to comprehensively explore treatment effectiveness. Future work should incorporate clinically validated measures of sleep health to better evaluate this outcome. Support (if any):
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".