Opioid-sparing effects of medical cannabis or cannabinoids for chronic pain: a systematic review and meta-analysis of randomised and observational studies
Bibliographic record
Abstract
OBJECTIVE: To assess the efficacy and harms of adding medical cannabis to prescription opioids among people living with chronic pain. DESIGN: Systematic review. DATA SOURCES: CENTRAL, EMBASE and MEDLINE. MAIN OUTCOMES AND MEASURES: Opioid dose reduction, pain relief, sleep disturbance, physical and emotional functioning and adverse events. STUDY SELECTION CRITERIA AND METHODS: We included studies that enrolled patients with chronic pain receiving prescription opioids and explored the impact of adding medical cannabis. We used Grading of Recommendations Assessment, Development and Evaluation to assess the certainty of evidence for each outcome. RESULTS: Eligible studies included five randomised trials (all enrolling chronic cancer-pain patients) and 12 observational studies. All randomised trials instructed participants to maintain their opioid dose, which resulted in a very low certainty evidence that adding cannabis has little or no impact on opioid use (weighted mean difference (WMD) -3.4 milligram morphine equivalent (MME); 95% CI (CI) -12.7 to 5.8). Randomised trials provided high certainty evidence that cannabis addition had little or no effect on pain relief (WMD -0.18 cm; 95% CI -0.38 to 0.02; on a 10 cm Visual Analogue Scale (VAS) for pain) or sleep disturbance (WMD -0.22 cm; 95% CI -0.4 to -0.06; on a 10 cm VAS for sleep disturbance; minimally important difference is 1 cm) among chronic cancer pain patients. Addition of cannabis likely increases nausea (relative risk (RR) 1.43; 95% CI 1.04 to 1.96; risk difference (RD) 4%, 95% CI 0% to 7%) and vomiting (RR 1.5; 95% CI 1.01 to 2.24; RD 3%; 95% CI 0% to 6%) (both moderate certainty) and may have no effect on constipation (RR 0.85; 95% CI 0.54 to 1.35; RD -1%; 95% CI -4% to 2%) (low certainty). Eight observational studies provided very low certainty evidence that adding cannabis reduced opioid use (WMD -22.5 MME; 95% CI -43.06 to -1.97). CONCLUSION: CRD42018091098.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".