Fibromyalgia and Disability Adjudication: No Simple Solutions to a Complex Problem
Bibliographic record
Abstract
BACKGROUND: Adjudication of disability claims related to fibromyalgia (FM) syndrome can be a challenging and complex process. A commentary published in the current issue of Pain Research & Management makes suggestions for improvement. The authors of the commentary contend that: previously and currently used criteria for the diagnosis of FM are irrelevant to clinical practice; the opinions of family physicians should supersede those of experts; there is little evidence that trauma can cause FM; no formal instruments are necessary to assess disability; and many FM patients on or applying for disability are exaggerating or malingering, and tests of symptoms validity should be used to identify malingerers. OBJECTIVES: To assess the assertions made by Fitzcharles et al. METHODS: A narrative review of the available research literature was performed. RESULTS: Available diagnostic criteria should be used in a medicolegal context; family physicians are frequently uncertain about FM and⁄or biased; there is considerable evidence that trauma can be a cause of FM; it is essential to use validated instruments to assess functional impairment; and the available tests of physical effort and symptom validity are of uncertain value in identifying malingering in FM. CONCLUSIONS: The available evidence does not support many of the suggestions presented in the commentary. Caution is advised in adopting simple solutions for disability adjudication in FM because they are generally incompatible with the inherently complex nature of the problem.
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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 | 0.056 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".