CNS Volume 14 supplement 16 Cover and Front matter
Bibliographic record
Abstract
Fibromyalgia (FM) is a common condition in the population occurring in 2% to 4% of adults living in the United States.Characterized by chronic widespread pain for at least 3 months and the presence of widespread mechanical tenderness, FM typically affects women and can be found in patients of varying ages who present in both primary care and psychiatric settings.As there are a number of other clinical syndromes which often occur with FM, it is important for clinicians to have a current understanding of the etiology of the syndrome and its diagnostic criteria.Conditions that occur comorbid with FM include lupus, rheumatoid arthritis, Sjogren's syndrome, and osteoarthritis, among others.Due to its chronic nature, FM often occurs highly comorbid with anxiety and depression, which can also worsen patient pain ratings.The optimal management of FM is comprised of both pharmacologic and nonpharmacologic approaches, including use of serotonin-norepinephrine reuptake inhibitors and/or cognitive-behavioral therapy.In this Expert Review Supplement, Roland Staud, MD, reviews the clinical manifestations of FM and provides an overview of the pain mechanisms in FM, prevalence of the syndrome, and current thinking on deficiencies in pain centers involved in FM; Philip J. Mease, MD, reviews comorbidities that commonly occur with FM, assessment of FM, and well-studied drug therapies targeting symptoms of FM; and David A. Williams, PhD, provides a rationale for optimal care involving a combination of pharmacologic and non-pharmacologic interventions.Lastly, a case study related to the overall diagnosis and treatment of FM in a typical patient is presented and discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.765 | 0.591 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".