Patient-Reported Impact of Symptoms in Fibromyalgia (PRISM-FM)
Bibliographic record
Abstract
OBJECTIVE: To identify the frequency and relative importance of symptoms experienced by adults with fibromyalgia (FM) and determine factors associated with a higher disease burden. METHODS: We conducted semistructured interviews with 15 participants with FM, collecting 1479 quotes regarding the symptomatic burden of FM. We then performed an international cross-sectional study involving 1085 participants with FM to determine the prevalence and relative importance (scale 0-4) of 149 symptoms representing 14 symptomatic themes. We performed subgroup analysis to determine how age, sex, disease duration, medication use, employment status, change in employment status, missing work due to FM, and ability level are related to symptomatic theme prevalence. RESULTS: The symptomatic themes with the highest prevalence in FM were pain (99.8%), muscle tenderness (99.8%), and fatigue (99.3%). The symptomatic themes that had the greatest effect on patients' lives were related to fatigue (2.88), pain (2.85), muscle tenderness (2.79), and impaired sleep and daytime sleepiness (2.70). Symptomatic theme prevalence was most strongly associated with the modified Rankin Scale level of disability, disability status, and change in employment status (on disability vs not on disability). CONCLUSION: Participants with FM identify a variety of symptoms that significantly affect their daily lives. Many of these symptoms, such as fatigue, sleep disturbance, and activity limitation, are life-altering and not related to traditional diagnostic criteria. Symptom prevalence in this population varies across subgroups based on demographic categories and disability status.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".