Efficacy of topical cannabinoids in the management of pain: a systematic review and meta-analysis of animal studies
Bibliographic record
Abstract
BACKGROUND/IMPORTANCE: Cannabinoids are emerging as an alternative pain management option, preliminarily supported by preclinical and clinical studies. Unwanted side effects from oral or inhaled cannabinoids remain, however, a major barrier to widespread use. Peripherally acting cannabinoids (eg, topically applied) may circumvent these side effects while providing localized pain management. OBJECTIVE: Our purpose was to systematically review the literature on the effectiveness of peripherally acting cannabinoids for pain management. EVIDENCE REVIEW: We searched MEDLINE, EMBASE, CENTRAL, CINAHL, and PubMed databases. Included studies examined the effect of topical/peripherally administered cannabinoids on pain ratings in humans, as well as pain-related outcomes in animals (eg, paw withdrawal). Due to a lack of trials, human studies were summarized in a narrative synthesis. Separate meta-analyses were performed for animal studies using radiant tail flick or paw withdrawal outcomes. FINDINGS: Our search yielded 1182 studies following removal of duplicates, with 46 studies (6 human, 40 animal) included. Human studies (one randomized controlled trial and five case studies/series) reported no adverse events to topical cannabinoids and preliminary evidence of decreased pain ratings. Animal studies reporting tail flick (5) (2.81, 95% CI 1.93 to 3.69, p<0.001) and mechanical withdrawal (11) (2.74, 95% CI 1.82 to 3.67, p<0.001) reported prolonged responses (analgesia) in peripheral cannabinoid groups compared with controls. CONCLUSIONS: Preclinical animal studies provided low-quality evidence for peripherally administered cannabinoids to provide regional, antinociceptive effects. The scarcity of high-quality human studies underscores the need to translate preclinical evidence into well-controlled human trials.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".