Cannabinoids, cannabis, and cannabis-based medicines for pain management: an overview of systematic reviews
Bibliographic record
Abstract
ABSTRACT: Cannabinoids, cannabis, and cannabis-based medicines (CBM) are increasingly used to manage pain, with limited understanding of their efficacy and safety. We assessed methodological quality, scope, and results of systematic reviews of randomised controlled trials of these treatments. Several search strategies sought self-declared systematic reviews. Methodological quality was assessed using both AMSTAR-2 and techniques important for bias reduction in pain studies. Of the 106 articles read, 57 were self-declared systematic reviews, most published since 2010. They included any type of cannabinoid, cannabis, or CBM, at any dose, however administered, in a broad range of pain conditions. No review examined the effects of a particular cannabinoid, at a particular dose, using a particular route of administration, for a particular pain condition, reporting a particular analgesic outcome. Confidence in the results in the systematic reviews using AMSTAR-2 definitions was critically low (41), low (8), moderate (6), or high (2). Few used criteria important for bias reduction in pain. Cochrane reviews typically provided higher confidence; all industry-conflicted reviews provided critically low confidence. Meta-analyses typically pooled widely disparate studies, and, where assessable, were subject to potential publication bias. Systematic reviews with positive or negative recommendation for use of cannabinoids, cannabis, or CBM in pain typically rated critically low or low (24/25 [96%] positive; 10/12 [83%] negative). Current reviews are mostly lacking in quality and cannot provide a basis for decision-making. A new high-quality systematic review of randomised controlled trials is needed to critically assess the clinical evidence for cannabinoids, cannabis, or CBM in 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 | 0.024 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".