The Neuropathic Pain Features in Psoriatic Arthritis: A Cross-sectional Evaluation of Prevalence and Associated Factors
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence and factors associated with the neuropathic pain features in a cohort of patients with psoriatic arthritis (PsA). METHODS: A cross-sectional evaluation was conducted in consecutive patients having PsA with prevalent peripheral joint involvement, referred to 3 rheumatological centers. For each patient, a comprehensive assessment of disease activity, physical function, and disease effect was carried out. The presence of comorbid fibromyalgia syndrome (FMS) was evaluated. Acute-phase reactants were also recorded. The neuropathic pain features were investigated through the PainDETECT Questionnaire (PDQ). A logistic regression analysis was therefore conducted using the PDQ as the dependent variable. RESULTS: The final evaluation included 118 patients. A comorbid FMS was detectable in 30 of the 118 patients with PsA (25.4%). Probable characteristics of neuropathic pain (PDQ ≥ 19) were found in 30 (25.4%) patients overall, ambiguous (PDQ > 12 and < 19) in 21 (17.8%) patients, and unlikely (PDQ ≤ 12) in 67 (56.8%) patients. Using logistic regression analysis, the only independent variable among those investigated that could explain the neuropathic pain features was the presence of a comorbid FMS (p = 0.0127). Excluding patients with comorbid FMS, an association with disability (measured by Health Assessment Questionnaire-Disability Index) emerges (p = 0.0489). In patients with PsA and comorbid FMS, PDQ scores were significantly higher than in patients without comorbid FMS. CONCLUSION: Neuropathic pain features are common in patients with PsA, and the presence of pain sensitization (comorbid FMS) seems to be its main predictor.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".