The impact of psoriasis on wellbeing and clinical outcomes in juvenile psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: Juvenile PsA (JPsA) has varied clinical features that are distinctive from other JIA categories. This study investigates whether such features impact patient-reported and clinical outcomes. METHODS: Children and young people (CYP) were selected if recruited to the Childhood Arthritis Prospective Study, a UK multicentre JIA inception cohort, between January 2001 and March 2018. At diagnosis, patient/parent-reported outcomes (as age-appropriate) included the parental global assessment (10 cm visual analogue scale), functional ability (Childhood Health Assessment Questionnaire (CHAQ)), pain (10 cm visual analogue scale), health-related quality of life (Child Health Questionnaire PF50 psychosocial score), mood/depressive symptoms (Moods and Feelings Questionnaire) and parent psychosocial health (General Health Questionnaire 30). Three-year outcome trajectories have previously been defined using active joint counts, physician and parent global assessments (PGA and PaGA, respectively). Patient-reported outcomes and outcome trajectories were compared in (i) CYP with JPsA vs other JIA categories and (ii) CYP within JPsA, with and without psoriasis via multivariable linear regression. RESULTS: There were no significant differences in patient-reported outcomes at diagnosis between CYP with JPsA and non-JPsA. Within JPsA, those with psoriasis had more depressive symptoms (coefficient = 9.8; 95% CI: 0.5, 19.0) than those without psoriasis at diagnosis. CYP with JPsA had 2.3 times the odds of persistent high PaGA than other ILAR categories, despite improving joint counts and PGA (95% CI: 1.2, 4.6). CONCLUSION: CYP with psoriasis at JPsA diagnosis report worse mood, supporting a greater disease impact in those with both skin and joint involvement. Multidisciplinary care with added focus to support wellbeing in children with JPsA plus psoriasis may help improve these outcomes.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".