Fibromyalgia: Presentation and Management with a Focus on Pharmacological Treatment
Bibliographic record
Abstract
Fibromyalgia is a condition with widespread muscle pain. Prevalence studies showed that 2% to 7% of the population have fibromyalgia, which affects approximately one million Canadians. Fibromyalgia is most common in women, but it also involves men and children. As with most chronic illnesses, the causes of fibromyalgia are unknown. However, recent research supports underlying abnormalities in the central nervous system, which supports fibromyalgia as a chronic disease state and valid clinical entity. Pain is the primary symptom, often accompanied by overwhelming fatigue, sleep dysfunction and cognitive impairment. In 1990, the American College of Rheumatology developed diagnostic criteria for the diagnosis of fibromyalgia. Lifestyle changes, including pacing of activities and aerobic exercise, are very important in managing fibromyalgia symptoms. Emotional and behavioural therapy can also be helpful. Controlled trials of antidepressants, gabapentinoids, tramadol, zopiclone and sodium oxybate have shown effectiveness in fibromyalgia patients. Pregabalin and duloxetine were recently approved in the United States. Effective management of fibromyalgia is complex and requires a multidisciplinary treatment approach. Response and tolerance of different therapeutic interventions vary from patient to patient. Recent advances in the pathophysiology of fibromyalgia offer hope for new and improved therapies in the management of this disabling condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".