Disability in Fibromyalgia Associates with Symptom Severity and Occupation Characteristics
Bibliographic record
Abstract
OBJECTIVE: It is intuitive that disability caused by illness should be reflected in illness severity. Because disability rates for fibromyalgia (FM) are high in the developed world, we have examined disease and work characteristics for patients with FM who were working, unemployed, or receiving disability payments for disability as a result of FM. METHODS: Of the 248 participants in a tertiary care cohort study of patients with FM, 90 were employed, 81 were not employed and not receiving disability payments, and 77 were not working and currently receiving disability payments awarded for disability caused by FM. Demographic, occupation, and disease characteristics were compared among the groups. RESULTS: The prevalence of disability caused by FM was 30.8%. There were no demographic differences among the working, unemployed, or disabled patients. With the exception of measures for anxiety and depression, all measurements for disease severity differed significantly among the groups, with greater severity reported for the disabled group, which used more medications and participated less in physical activity. Disabled patients were more likely previously employed in manual professions or the service industry, whereas employed patients were more commonly working in non-manual jobs that included clerical, managerial, or professional occupations (p = 0.005). CONCLUSION: The one-third rate of disability for this Canadian cohort of patients with FM is in line with other reports from the western world. Associations of disability compensation were observed for subjective report of symptom severity, increased use of medications, and previous employment in more physically demanding jobs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".