Rheumatologists lack confidence in their knowledge of cannabinoids pertaining to the management of rheumatic complaints
Bibliographic record
Abstract
BACKGROUND: Arthritis pain is reported as one of the most common reasons for persons using medical herbal cannabis in North America. "Severe arthritis" is the condition justifying legal use of cannabis in over half of all authorizations in Canada, where cannabis remains a controlled substance. As champions for the care of persons with arthritis, rheumatologists must be knowledgeable of treatment modalities both traditional and non-traditional, used by their patients. As study of cannabinoid molecules in medicine is recent, we have examined the confidence in the knowledge of cannabinoids expressed by Canadian rheumatologists. METHODS: The confidence of rheumatologists in their knowledge of cannabinoid molecules and mechanisms relevant to rheumatology, and their ability to advise patients about cannabinoid treatments was recorded by an online questionnaire circulated via email to the entire Canadian Rheumatology Association membership. RESULTS: Over three quarters of the 128 respondents lacked confidence in their knowledge of cannabinoid molecules. While 45% of respondents believed there was no current role for cannabinoids in rheumatology patient care, only 25% supported any use of herbal cannabis. With 70% never having previously prescribed or recommended any cannabinoid treatment, uncertainty regarding good prescribing practices was prevalent. Concerns about risks of cannabis use were in line with the current literature. CONCLUSIONS: Rheumatologists lacked confidence in their knowledge of cannabinoid molecules in general and in their competence to prescribe any cannabinoid for rheumatic complaints. In line with this uncertainty, there is reticence to prescribe cannabinoid preparations for rheumatology patients. Guidance is required to inform rheumatologists on the evidence regarding cannabinoids.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".