Efficacy, tolerability and safety of cannabis‐based medicines for chronic pain management – An overview of systematic reviews
Bibliographic record
Abstract
Medicinal cannabis has already entered mainstream medicine in some countries. This systematic review (SR) aimed at evaluating the efficacy, acceptability and safety of cannabis-based medicines for chronic pain management. Qualitative systematic review of SRs of randomized controlled trials with cannabis-based medicines for chronic pain management. The Cochrane databases of SRs, Database of Abstracts of Reviews of Effects and PubMed were searched for SR published in the period January 2009 to January 2017. Assessment of the methodological quality of SR was performed by the AMSTAR checklist. Out of 748 papers identified, 10 SRs met the inclusion criteria. The methodological quality was high in four and moderate in six SRs. There were inconsistent findings of four SRs on the efficacy of cannabis-based medicines in neuropathic pain and of one SR for painful spasms in multiple sclerosis. There were consistent results that there was insufficient evidence of any cannabis-based medicine for pain management in patients with rheumatic diseases (three SRs) and in cancer pain (two SRs). Cannabis-based medicines undoubtedly enrich the possibilities of drug treatment of chronic pain conditions. It remains the responsibility of the health care community to continue to pursue rigorous study of cannabis-based medicines to provide evidence that meets the standard of 21st century clinical care. SIGNIFICANCE: We provide an overview of systematic reviews on the efficacy, tolerability and safety of cannabis-based medicines for chronic pain management. There are inconsistent findings of the efficacy of cannabinoids in neuropathic pain and painful spasms in multiple sclerosis. There are inconsistent results on tolerability and safety of cannabis-based medicines for any chronic 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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".