Medical cannabis for orthopaedic patients with chronic musculoskeletal pain: does evidence support its use?
Bibliographic record
Abstract
The treatment of chronic, non-cancer musculoskeletal pain has become a topic growing interest as it is believed to be one of the reasons for the current opioid epidemic. The medicinal use of cannabis has a long history as a number of active compounds in cannabis have been shown to interact with the body's endocannabinoid system to reduce pain. This position paper provides a history on the evolution of cannabis, the science behind its therapeutic effects, and review of the evidence and current guideline recommendations on its use as a treatment for patients with chronic, non-cancer musculoskeletal pain. Results from systematic reviews have demonstrated a statistically significant reduction in chronic pain conditions with cannabinoids, compared with placebo, although the effects might be considered small and did not reach the minimally important difference. More adverse events were reported in the cannabinoid group than in the placebo group with longer than 2 weeks of treatment. There is a lack of evidence on dependence. With changes to policies, patients' perception has changed to be more positive toward the use of medical cannabis. Current recommendations from North America, Latin America, Europe, Australia and Iran support the use of medical cannabis for chronic, non-cancer pain. Based on the current evidence, it is our position that cannabinoids may be considered as an adjunctive therapy after recommended first- and second-line therapies have failed to provide sufficient efficacy or tolerability. Patients should consider the balance between the desirable and undesirable effects of taking cannabis for chronic pain, and comprehensively consider their own values and preferences, as well as cost-effectiveness factors, based on the information provided by their physician.
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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".