A Medicolegal Analysis of Worker Appeals for Fibromyalgia as a Compensable Condition Following Workplace Soft-tissue Injury
Bibliographic record
Abstract
OBJECTIVE: Workplace injuries may be implicated in the causation of fibromyalgia (FM), hence linking FM to compensation. We examined the appeals by workers directed to an appeals tribunal for causation of FM following soft-tissue injury sustained in the workplace. METHODS: One hundred fifty tribunal decisions relevant to FM were examined using a predetermined protocol. New-onset FM was appealed in 123, and aggravation of preexisting FM in 15. RESULTS: All injuries were of a soft-tissue type, without persistent physical findings to explain continued symptoms. The tribunal accepted 67% of appeals for aggravation of FM, and 59% for new-onset FM. Time from injury to FM diagnosis was 4.3 ± 4.1 years, with 6.3 ± 2.8 physicians cited for each worker, and with previous health status not reported for 26%. Injuries were a single event in 68%, with location in low back for 44%, and shoulder/upper limb in 40%. The FM diagnosis was based on a rheumatologist report in 74%. CONCLUSION: Over half of appeals for aggravation or causation of FM following a work-related soft-tissue injury were accepted by the tribunal, with importance ascribed to a rheumatologist diagnosis. Concerns are raised regarding lengthy duration from injury to diagnosis, claimants' high healthcare use, and neglect of mention of previous health status. The attribution of causation of FM to a soft-tissue workplace traumatic event is contentious and requires further examination.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".