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 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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.036 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".