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Record W3007289662 · doi:10.1177/1179544120906461

Cannabinoids in Chronic Non-Cancer Pain: A Systematic Review and Meta-Analysis

2020· review· en· W3007289662 on OpenAlexaff
Herman Johal, Tahira Devji, Yaping Chang, Jonathan J. Simone, Christopher Vannabouathong, Mohit Bhandari

Bibliographic record

VenueClinical Medicine Insights Arthritis and Musculoskeletal Disorders · 2020
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMeta-analysisMedicineChronic painBioinformaticsSystematic reviewCancerMEDLINEInternal medicinePhysical therapyBiology

Abstract

fetched live from OpenAlex

Background: For patients with chronic, non-cancer pain, traditional pain-relieving medications include opioids, which have shown benefits but are associated with increased risks of addiction and adverse effects. Medical cannabis has emerged as a treatment alternative for managing these patients and there has been a rise in the number of randomized clinical trials in recent years; therefore, a systematic review of the evidence was warranted. Objective: To analyze the evidence surrounding the benefits and harms of medical cannabinoids in the treatment of chronic, non-cancer-related pain. Design: Systematic review with meta-analysis. Data sources: Medline, Embase, CINAHL, SCOPUS, Google Scholar, and Cochrane Databases. Eligibility criteria: English language randomized clinical trials of cannabinoids for the treatment of chronic, non-cancer-related 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 6 reviewers. Data were pooled through meta-analysis with different durations of treatment (2 weeks, 2 months, 6 months) and stratified by route of administration (smoked, oromucosal, oral), conditions, and type of cannabinoids. Main outcomes and measures: Patient-reported pain and adverse events (AEs). Results: Thirty-six trials (4006 participants) were included, examining smoked cannabis (4 trials), oromucosal cannabis sprays (14 trials), and oral cannabinoids (18 trials). Compared with placebo, cannabinoids showed a significant reduction in pain which was greatest with treatment duration of 2 to 8 weeks (weighted mean difference on a 0-10 pain visual analogue scale −0.68, 95% confidence interval [CI], −0.96 to −0.40, I 2 = 8%, P < .00001; n = 16 trials). When stratified by route of administration, pain condition, and type of cannabinoids, oral cannabinoids had a larger reduction in pain compared with placebo relative to oromucosal and smoked formulations but the difference was not significant ( P[interaction] > .05 in all the 3 durations of treatment); cannabinoids had a smaller reduction in pain due to multiple sclerosis compared with placebo relative to other neuropathic pain ( P[interaction] = .05) within 2 weeks and the difference was not significant relative to pain due to rheumatic arthritis; nabilone had a greater reduction in pain compared with placebo relative to other types of cannabinoids longer than 2 weeks of treatment but the difference was not significant ( P[interaction] > .05). Serious AEs were rare, and similar across the cannabinoid (74 out of 2176, 3.4%) and placebo groups (53 out of 1640, 3.2%). There was an increased risk of non-serious AEs with cannabinoids compared with placebo. Conclusions: There was moderate evidence to support cannabinoids in treating chronic, non-cancer pain at 2 weeks. Similar results were observed at later time points, but the confidence in effect is low. There is little evidence that cannabinoids increase the risk of experiencing serious AEs, although non-serious AEs may be common in the short-term period following use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0210.005
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.062
GPT teacher head0.420
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations105
Published2020
Admission routes1
Has abstractyes

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