Large joint and lower extremity involvement have higher impact on disease outcomes in oligoarticular psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVE: Joints with different sizes and anatomical locations can be affected in psoriatic arthritis (PsA). Our aim was to explore the effect of different joint patterns on patient-reported outcomes (PROs) in patients with mono-oligoarthritis. METHODS: Within PsArt-ID (Psoriatic Arthritis- International Database), 387/1670 patients who had mono-oligoarthritis (1-4 tender and swollen joints) were enrolled in cross-sectional assessment. The joints were categorized according to their size (small/large) and location (upper/lower extremity) and PROs, physician global assessment and C-reactive protein (CRP) were compared. Analysis was made by categorizing according to joint counts (1-2 joints/ 3-4 joints). RESULTS: The mean age (SD) was 46.9 (14.24) with a mean (SD) PsA duration of 3.93 (6.03) years. Within patients with 1-2 involved joints (n = 302), size of the joints only had an impact on CRP values with large joints having higher CRP (P = .005), similar to lower extremity involvement (P = .004). PROs were similar regardless of size or location if 1-2 joints were inflamed. Within patients with 3-4 involved joints (n = 85), patient global assessment (PGA), pain, fatigue and physician global assessment were higher in the group with large joints. Similarly, PGA, pain, and physician global assessment were higher in patients with lower extremity involvement as well as higher CRP values. CONCLUSION: For PsA patients with 3-4 joints involved, lower extremity and large joints are associated with poorer outcomes with worse PROs, physician global assessment, and higher CRP. The size and anatomical location of the joints are less important for patients with 1-2 joints in terms of the PROs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".