Use of medical cannabis by patients with fibromyalgia in Canada after cannabis legalisation: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: Medications have only small to moderate effects on symptoms in fibromyalgia (FM). Cannabinoids, including medical cannabis (MC) may have potential to fill this gap. Since recreational legalisation of cannabis in Canada, patients have easier access and may be self-medicating with cannabis. We have examined the prevalence and characteristics of MC use in FM patients. METHODS: During a two-month period (June-August 2019), consecutive attending rheumatology patients participated in an onsite survey comprising 2 questionnaires: 1) demographic and disease information completed by the rheumatologist, 2) patient anonymous questionnaire of health status, cannabis use (recreational and/or medicinal) and characteristics of use. RESULTS: In a cohort of 1000 rheumatology attendees, 117 (11.7%) were diagnosed with FM. Ever use of MC was reported by 28 (23.9%; 95%CI: 16.5%-32.7%) FM patients compared to 98 (11.1%; 95%CI: 9.1%-13.4%) non-FM patients. Among FM ever users, 17 (61%) patients continued use of MC. FM ever users vs. FM nonusers tended to be younger, 53 vs. 58 years (p=0.072), were more likely unemployed or disabled 39% vs. 17% (p=0.019) and used more medication types (p=0.013) but did not differ in symptom severity parameters. Cigarette smoking and recreational cannabis were more common in ever users. Global symptom relief on a VAS (1-10) was 7.0±2.3. CONCLUSIONS: FM patients have commonly used MC, with more than half continuing use. Reported symptom relief was substantial. Cigarette smoking and recreational cannabis use may play a facilitatory role in MC use in FM. Adjunctive MC may be a treatment consideration for some FM patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".