Pain Medications Used by Persons Living With Fibromyalgia: A Comparison Between the Profile of a Quebec Sample and Clinical Practice Guidelines
Bibliographic record
Abstract
Background Pharmacological management of fibromyalgia is complex. Chronic pain management is characterized by off-label prescribing and use, multimorbidity, and polypharmacy.Aims This study aimed to describe pain medications use and perceived risk among people living with fibromyalgia and compare this use to evidence-based recommendations.Methods Directive telephone interviews were conducted with 63 individuals self-reporting a diagnosis of fibromyalgia (Quebec, Canada). The questionnaire addressed specific questions about their pain and pharmacological treatments currently used for pain management (prescribed and over-the-counter). Collected data were compared to the Canadian Fibromyalgia Clinical Practice Guidelines and to evidence reports published by recognized organizations.Results Despite a lack of robust scientific evidence to support opioids use to manage pain in fibromyalgia, 33% of our sample report using them. Non-steroidal anti-inflammatory drugs were used by 54.0% of participants, although this medication is not recommended due to lack of efficacy. Tramadol, which is recommended, was used by 23.8% of participants. Among the medications strongly recommended, anticonvulsants were used by 36.5%, serotonin-norepinephrine reuptake inhibitor antidepressants by 55.6%, and tricyclic antidepressants by 22.2%. Cannabinoids (17.5%) and medical cannabis use (34.9%) was also reported. For all these medication subclasses, no differences were found between participants not reporting (n=35) or reporting (n=28) more than one pain diagnosis (p<.05). Medication subclasses considered most at risk of adverse effects by participants were the least used.Conclusions Results reveal discordance between evidence-based recommendations and medications use, which highlights the complexity of pharmacological treatment of fibromyalgia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".