Cannabinoids in the Management of Acute Pain: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective: To synthesize the best evidence surrounding the efficacy of cannabinoids for acute pain in the clinical setting based on subjective pain scores and observed adverse effects. Design: Systematic review with meta-analysis. Data Sources: PubMed, Embase, Cochrane Databases, and Google Scholar. Eligibility Criteria: English-language randomized-controlled clinical trials comparing cannabinoids with placebo in patients with acute pain. Data Extraction and Synthesis: Study quality was assessed using the Cochrane risk of bias tool. All stages were conducted independently by a team of three reviewers. Data were pooled through meta-analysis and stratified by route of administration. Primary Outcomes and Measures: Patient-reported pain and adverse events (AEs). Results: Six trials (678 participants) were included examining oral (5 trials) and intramuscular (1 trial) cannabinoids. Overall, there was a small but statistically significant treatment effect favoring the use of cannabinoids over placebo (−0.90, 95% confidence interval [CI] −1.69 to −0.1, i 2 =65%, p =0.03). When stratified by route of administration, intramuscular cannabinoids were found to have a significant reduction in pain relative to placebo (−2.98, 95% CI −4.09 to −1.87, i 2 =0%, p <0.0001). No difference in effect was observed between oral cannabinoids and placebo (−0.21, 95% CI −0.64 to 0.22, i 2 =3%, p =0.34). Serious AEs were rare, and similar across the cannabinoid (14/374, 3.7%) and placebo groups (8/304, 2.6%). Conclusions: There is low-quality evidence indicating that cannabinoids may be a safe alternative for a small but significant reduction in subjective pain score when treating acute pain, with intramuscular administration resulting in a greater reduction relative to oral. Higher quality, long-term randomized-controlled trials examining whether there may be a role for cannabinoids in treating acute pain are required.
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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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.004 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".