Association of herbal cannabis use with negative psychosocial parameters in patients with fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: Patients with chronic pain, including fibromyalgia (FM), may seek treatments outside of mainstream medicine. Medicinal cannabinoids are popularly advocated for pain relief but with limited evidence for efficacy in FM. The extent of use of cannabinoids in FM is unknown. METHODS: We have documented the self-reported prevalence of cannabinoid use in 457 patients with the diagnosis of FM and referred to a tertiary care pain center. We validated the diagnosis of FM and examined the associations of cannabinoid use in these patients. RESULTS: Cannabinoids were being used by 13% of all patients, of whom 80% used herbal cannabis (marijuana), 24% used prescription cannabinoids, and 3% used both herbal cannabis and prescription cannabinoids. One-third of all men used cannabinoids. Current unstable mental illness (36% versus 23%; P = 0.002), opioid drug-seeking behavior (17% versus 4%; P = 0.002), and male sex (26% versus 7%; P = 0.0002) were all associated with herbal cannabis use. There was a trend for cannabinoid users to be unemployed and receiving disability payments. The diagnosis of FM was validated in 302 patients, with 155 assigned another primary diagnosis. When the FM group was analyzed separately, significant associations were lost, but trends remained. CONCLUSION: Cannabinoids were used by 13% of patients referred with a diagnosis of FM. The association of herbal cannabis use with negative psychosocial parameters raises questions regarding the motive for this self-medication practice. Although cannabinoids may offer some therapeutic effect, caution regarding any recommendation should be exercised pending clarification of general health and psychosocial problems, especially for those self-medicating.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".