Cannabis for medical use versus opioids for chronic non-cancer pain: a systematic review and network meta-analysis of randomised clinical trials
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to evaluate the comparative benefits and harms of opioids and cannabis for medical use for chronic non-cancer pain. DESIGN: Systematic review and network meta-analysis. DATA SOURCES: EMBASE, MEDLINE, CINAHL, AMED, PsycINFO, PubMed, Web of Science, Cannabis-Med, Epistemonikos and the Cochrane Library (CENTRAL) from inception to March 2021. STUDY SELECTION: Randomised trials comparing any type of cannabis for medical use or opioids, against each other or placebo, with patient follow-up ≥4 weeks. DATA EXTRACTION AND SYNTHESIS: Paired reviewers independently extracted data. We used Bayesian random-effects network meta-analyses to summarise the evidence and the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach to evaluate the certainty of evidence and communicate our findings. RESULTS: Ninety trials involving 22 028 patients were eligible for review, among which the length of follow-up ranged from 28 to 180 days. Moderate certainty evidence showed that opioids provide small improvements in pain, physical functioning and sleep quality versus placebo; low to moderate certainty evidence supported similar effects for cannabis versus placebo. Neither was more effective than placebo for role, social or emotional functioning (all high to moderate certainty evidence). Moderate certainty evidence showed there is probably little to no difference between cannabis for medical use and opioids for physical functioning (weighted mean difference (WMD) 0.47 on the 100-point 36-item Short Form Survey physical component summary score, 95% credible interval (CrI) -1.97 to 2.99), and cannabis resulted in fewer discontinuations due to adverse events versus opioids (OR 0.55, 95% CrI 0.36 to 0.83). Low certainty evidence suggested little to no difference between cannabis and opioids for pain relief (WMD 0.23 cm on a 10 cm Visual Analogue Scale (VAS), 95% CrI -0.06 to 0.53) or sleep quality (WMD 0.49 mm on a 100 mm VAS, 95% CrI -4.72 to 5.59). CONCLUSIONS: Cannabis for medical use may be similarly effective and result in fewer discontinuations than opioids for chronic non-cancer pain. PROSPERO REGISTRATION NUMBER: CRD42020185184.
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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 | 0.042 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.042 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".