Systematic Review and Meta-analysis of Cannabis Treatment for Chronic Pain
Bibliographic record
Abstract
SETTING: Cannabis preparations have been used as a remedy for thousands of years in traditional medicine. Clinical use of cannabinoid substances is restricted, due to legal and ethical reasons, as well as limited evidence showing benefits. OBJECTIVE: To assess the efficacy and harms of cannabis preparations in the treatment of chronic pain. DESIGN: Systematic review and meta-analysis of double-blind randomized controlled trials that compared any cannabis preparation to placebo among subjects with chronic pain. An electronic search was made in Medline/Pubmed, Embase, and The Cochrane Controlled Trials Register (TRIALS CENTRAL) of all literature published until February 2008, as well as specific web pages devoted to cannabis. Studies were cross-checked, selected, and assessed. RESULTS: Eighteen trials were included. The efficacy analysis (visual analog scales) displayed a difference in standardized means in favor of the cannabis arm of -0.61 (-0.84 to -0.37), with statistical homogeneity (I(2) = 0.0%; P = 0.50). For the analysis of harms, the following Odds Ratios (OR) and number needed to harm (NNH) were obtained: for events linked to alterations to perception, OR: 4.51 (3.05-6.66), NNH: 7 (6-9); for events affecting motor function, 3.93 (2.83-5.47), NNH: 5 (4-6); for events that altered cognitive function, 4.46 (2.37-8.37), NNH: 8 (6-12). CONCLUSIONS: Currently available evidence suggests that cannabis treatment is moderately efficacious for treatment of chronic pain, but beneficial effects may be partially (or completely) offset by potentially serious harms. More evidence from larger, well-designed trials is needed to clarify the true balance of benefits to harms.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".