Medical cannabis and its efficacy/effectiveness on the management of osteoarthritis pain and function
Bibliographic record
Abstract
OBJECTIVE: Despite pharmacological treatments for osteoarthritis (OA), more individuals are choosing medical cannabis for OA symptom management and for mitigating opioid prescriptions for OA. This systematic review examines the global evidence of medical cannabis use on OA pain and function. METHODS: The search was completed in MEDLINE (PubMed), Embase, and CINAHL within the past 10 years (2012-2022). We limited the search to English language articles. We did not include grey literature or case studies. Participant demographics included all adult individuals with OA who were using medical cannabis for OA. Study quality and risk of bias were evaluated using the Grading of Recommendations, Assessment, Development and Evaluations framework; and the Risk of Bias in Non-randomized Studies of Interventions tool. We used a narrative synthesis approach. RESULTS: Overall, 7 studies were included: 2 randomized controlled trials (RCT) and 5 observational studies. Only 1 of the 2 RCTs reported improvements in pain for cannabis users. All 5 observational studies reported an improvement in pain levels, reduction of opioid use, and/or improvement in overall OA function. Despite high risk of bias ratings and low study quality, the consensus across studies was that medical cannabis use was effective for a subgroup of individuals suffering from OA pain. CONCLUSIONS: There is low quality evidence to support medical cannabis use as a substitute for primary pharmacological treatment of OA. However, this does not negate the observations that medical cannabis may provide therapeutic relief for a subset of patients. SYSTEMATIC REVIEW PROPSERO REGISTRATION: CRD42022354026.
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 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 | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".