Cannabis Use in Knee Osteoarthritis
Bibliographic record
Abstract
Background Knee osteoarthritis (OA) is a prevalent and progressive joint disease, significantly impacting morbidity, disability, and healthcare utilization. Conventional treatments often fall short in managing knee OA effectively, prompting exploration into alternative therapies like cannabis. This project aimed to expand on the limited understanding of cannabis in managing knee OA. It aimed to characterize current management strategies and cannabis use patterns among individuals with knee OA, as well as to assess patient-perceived efficacy and tolerability of cannabis in knee OA. Methods An anonymous survey was distributed to patients through physician and nurse practitioner clinics in Saskatchewan. In addition to participant demographics, the questionnaire assessed disease severity and the treatments used to manage symptoms of OA. Specific questions were asked to participants who indicated they used cannabis to investigate formulations used, routes of administration, perceived effectiveness, and tolerability. Data from the survey was analyzed descriptively. Results Invitation packages were distributed to 205 people with knee OA from an orthopedic surgeon’s office and two primary healthcare centers. A total of 89 participants completed the survey achieving an overall response rate of 43.4%. The majority of participants were white (n=78, 87.6%), over 65 years old (n=58, 62.2%), and retired (n=61, 68.5%). Acetaminophen was the most commonly used pharmacologic agent with 60 participants (67.4%) reporting its use. It was followed by topical (n=46 participants, 51.7%) and oral non-steroidal anti-inflammatory drugs (NSAIDs) (n=39 participants, 43.8%). Nearly half of the respondents used exercise as a management strategy (n=42, 48.3%). Cannabis was currently being used to manage symptoms by 17.6% of the participants, with 62.5% reporting improvements in pain and 68.8% reporting improvements in sleep. Additionally, five cannabis users (31.3%) noted a reduction in the amount of other pain medications being used. There was significant variation in the dose, formulation, and route of administration of cannabis products used, and the majority of products were purchased from retail cannabis stores. Conclusion While conventional pharmacological treatments, particularly acetaminophen and NSAIDs, remain predominant in managing knee OA, a notable proportion of participants also reported using cannabis. Cannabis was perceived to be particularly effective for pain and sleep improvement, albeit with variable impacts on physical function, swelling, stiffness, and mental and social health and with a wide range of reported doses and formulations used. The lack of standardized information on safe and effective cannabis regimens for medical purposes presents a significant challenge for healthcare providers who must be equipped to facilitate conversations with patients, given the prevalence of cannabis use. Future research is crucial for determining the optimal formulation and dose of cannabis for managing knee OA and controlled clinical trials are necessary for establishing efficacy and safety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".