Cannabinoids, cannabis, and cannabis-based medicine for pain management: a systematic review of randomised controlled trials
Bibliographic record
Abstract
ABSTRACT: Cannabinoids, cannabis, and cannabis-based medicines (CBMs) are increasingly used to manage pain, with limited understanding of their efficacy and safety. We summarised efficacy and adverse events (AEs) of these types of drugs for treating pain using randomised controlled trials: in people of any age, with any type of pain, and for any treatment duration. Primary outcomes were 30% and 50% reduction in pain intensity, and AEs. We assessed risk of bias of included studies, and the overall quality of evidence using GRADE. Studies of <7 and >7 days treatment duration were analysed separately. We included 36 studies (7217 participants) delivering cannabinoids (8 studies), cannabis (6 studies), and CBM (22 studies); all had high and/or uncertain risk of bias. Evidence of benefit was found for cannabis <7 days (risk difference 0.33, 95% confidence interval 0.20-0.46; 2 trials, 231 patients, very low-quality evidence) and nabiximols >7 days (risk difference 0.06, 95% confidence interval 0.01-0.12; 6 trials, 1484 patients, very low-quality evidence). No other beneficial effects were found for other types of cannabinoids, cannabis, or CBM in our primary analyses; 81% of subgroup analyses were negative. Cannabis, nabiximols, and delta-9-tetrahydrocannabinol had more AEs than control. Studies in this field have unclear or high risk of bias, and outcomes had GRADE rating of low- or very low-quality evidence. We have little confidence in the estimates of effect. The evidence neither supports nor refutes claims of efficacy and safety for cannabinoids, cannabis, or CBM in the management of pain.
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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 | 0.023 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".