OA39 Assessment of pain types in recently diagnosed patients with inflammatory arthritis
Bibliographic record
Abstract
Abstract Background/Aims Up to 40% of patients with inflammatory arthritis (IA) experience persistent pain which, as the disease progresses, is likely to be underpinned by distinct peripherally versus centrally mediated mechanisms. This study assessed the sensory profiles of recently diagnosed individuals with IA, hypothesising that pain at this early stage is primarily driven by peripheral joint inflammation. Methods Patients with a recent IA diagnosis and a pain numerical rating scale (NRS) score of ≥ 3 were recruited for the study. Data collected included demographics, Disease Activity Score-28 (DAS28CRP), quality of life (measured by MSK-HQ and EQ-5D), mental health status (PHQ-ADS), musculoskeletal ultrasound findings, and pain characteristics (using painDETECT, fibromyalgia criteria, and static and dynamic quantitative sensory testing [QST]). Results 61 participants (57% female, 62% with rheumatoid arthritis) were analysed, with a mean age of 49.8 ± 15 years and an average time since diagnosis of 1.2 ± 2.3 months. The mean DAS28 score was 3.8 ± 1, and the mean NRS pain score was 5.5 ± 2.1. Criteria for anxiety, depression, and somatic symptoms were fulfilled in 62%, 66%, and 80% of participants, respectively. As expected, 97% had peripheral joint inflammation confirmed by clinical assessment and ultrasound. Unexpectedly, in addition, 20% had a tender-minus-swollen joint count of ≥ 7, 25% had a painDETECT score of ≥ 19, and 21% met the fibromyalgia criteria, indicating potential centrally mediated pain. These outcomes significantly correlated with DAS28, MSK-HQ, and PHQ-ADS scores. QST findings showed evidence of abnormal central pain processing in a subset of participants, where enhanced pain facilitatory and reduced pain inhibitory mechanisms were recorded. Conclusion This work provides the first detailed study of sensory profiles in individuals in the early stages of IA. Evidence of centrally mediated pain, present even at the time of diagnosis, was obtained. Our findings challenge the notion that, at the early stage of disease, pain is driven only by peripheral mechanisms. This has implications for pain relief therapeutic targets. Disclosure Z. Rutter-Locher: Grants/research support; NIHR301674. S. Norton: None. J. Taylor: None. B. Menon: None. T. Esterine: None. R. Williams: None. L. Taams: None. K. Bannister: None. B. Kirkham: None.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".