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 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".