Characteristics of pain and their relationship to disease activity in UK patients with Behçet’s syndrome: a prospective cohort study
Bibliographic record
Abstract
Background Behçet’s syndrome (BS) is a rare multi-systemic vasculitis of unknown aetiology. Fibromyalgia syndrome (FMS) is more prevalent in rheumatological conditions such-as BS, than the general population. However, there is limited research into the aetiology and characteristics of pain in BS. Objectives To describe the pain characteristics and incidence of FMS in people with BS and investigate their relationship with BS disease activity. Methods A cohort study of BS patients attending the Liverpool Behçet's Centre between February 2017 and March 2019. BS was defined using the International Study Group Criteria. BS severity was assessed using the Behçet's Disease Current Activity Form. FMS was determined from consultant diagnosis. Assessments of pain included: Pain Visual Analogue Scale (PVAS), Pain Mannequin, Brief Pain Inventory, EQ-5D-3L and Short Form McGill. Pain and FMS prevalence were compared between high and low disease activity. Results 90% reported moderate-severe pain with a median PVAS score of 68/100 [38, 81]. 35.6% of participants had FMS and 46.5% experienced generalized pain. 76% of participants with high disease activity reported severe pain, compared to 39.1% with low disease activity ( p = .003). Pain was more generalised in high disease activity (72%) compared to low disease activity (37.7%) ( p = .003). FMS was more prevalent in the high disease activity group (52%) than the low disease activity group (29%) ( p = .04). Conclusions This is the first study to explore pain in participants with BS in the United Kingdom. The majority of BS patients experience moderate-severe widespread pain. Severe widespread pain is more prevalent in those with high disease activity. We have demonstrated a relationship between high disease activity, worse pain intensity, and FMS. This paper contributes to the understanding of two conditions which remain to be fully understood, FMS and BS, and generates new hypotheses to describe the interplay between.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".