How do central sensitisation features affect symptoms among patients with rheumatoid arthritis? Analysis of pain descriptors and the effect of central sensitivity syndrome on patient and evaluator global assessments
Bibliographic record
Abstract
Central sensitivity syndrome (CSS) comprises various symptoms caused by central sensitisation (CS). Using the central sensitisation inventory (CSI), a screening questionnaire developed for detecting CSS, this syndrome was recently identified in patients with long-standing rheumatoid arthritis (RA). However, the descriptors of CS-related pain and the effects of CSS on symptoms in patients with rheumatoid arthritis (RA) remain unknown. We examined the characteristics of pain and influence of CSS on patient and evaluator global assessment among multiple clinical variables.We used the central sensitisation inventory (CSI) and short-form McGill pain questionnaire to evaluate CSS and characteristics of pain in 240 outpatients with RA. Disease activity, fibromyalgia, neuropathic pain, anxiety, depression, pain catastrophising, and health-related quality of life were evaluated. We used multivariate analysis to analyse the characteristics of CS-related pain according to CSI and the effect of CSS on patient global assessment (PGA), evaluator global assessment (EGA), and PGA minus EGA among relevant clinical variables.In patients with RA, the main descriptors of pain according to severity of CSI scores were "sharp" and "stabbing", whereas those of pain according to disease activity were "tender" and "throbbing". CSS was associated with EGA (p=0.000, β=- 0.199) and PGA minus EGA (p=0.021, β=0.147), but not with PGA.In patients with RA, descriptors for CS-related pain differ from those for disease activity-related pain. CSS may have an important impact on EGA and PGA minus EGA. Additionally, CSI may be helpful in identifying why there is discordance between PGA and EGA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".