Comorbid fibromyalgia: A qualitative review of prevalence and importance
Bibliographic record
Abstract
Fibromyalgia (FM) may be an unrecognized cause of suffering for persons with an array of medical conditions. This is especially true for illness that is characterized by pain of any nature. Once believed to be a unique diagnosis, FM is recently reported to occur concomitantly with various rheumatic diseases, and importantly adversely impacts global health status. However, there is increasing report of FM associated with other diseases that are not defined by chronic pain. This qualitative review examines the evidence for comorbid FM in illness, and where available the effect of FM on the primary disease. Other than for musculoskeletal disorders, the published literature reporting an association of FM with illness is limited with scanty reports for some neurological, gastrointestinal, mental health and other overlapping pain conditions. Comorbid FM adversely affects both health status and outcome for rheumatic diseases, but with limited study in other diseases. When unrecognized, comorbid FM may be mistaken as poor control of the primary disease, leading to incorrect treatment decisions. FM may be a neglected condition that pervades many conditions and may contribute to the burden of illness. Physicians should be alert to the possibility of comorbid FM, and symptoms of FM should be specifically addressed. SIGNIFICANCE: Comorbid fibromyalgia (FM) in other medical conditions is largely unrecognized. When reported as accompanying rheumatic diseases, FM adversely affects global health status. With limited reports of comorbid FM with other conditions, neglect to diagnose comorbid FM may misdirect treatments.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".