Patterns of joint involvement at onset differentiate oligoarticular juvenile psoriatic arthritis from pauciarticular juvenile rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVE: To compare the patterns of joint involvement of patients with oligoarticular onset juvenile psoriatic arthritis (Oligo-JPsA) and pauciarticular onset juvenile rheumatoid arthritis (Pauci-JRA) in order to estimate the predictive performance of specific patterns for the diagnosis of Oligo-JPsA. METHODS: Twenty-three children who fulfilled the diagnostic criteria for JPsA (Vancouver criteria) and who had fewer than 5 joints involved in the first 6 months of disease (Oligo-JPsA), and 64 children with Pauci-JRA (ACR criteria) were enrolled. Patients were also classified with respect to the ILAR criteria for juvenile idiopathic arthritis (JIA). Patient characteristics and clinical features at onset and during followup were determined. Patterns of joint involvement at onset of disease and their ability to differentiate between Oligo-JPsA and Pauci-JRA/Oligo-JIA were evaluated. RESULTS: Small joint disease (defined as involvement of any of the metatarsophalangeal or proximal or distal interphalangeal joints of the foot, or metacarpophalangeal or proximal or distal interphalangeal joints of the hand) was significantly more frequent in Oligo-JPsA than in Pauci-JRA at disease onset. The odds of patients with Oligo-JPsA having small joint disease or wrist disease within 6 months of disease onset were much higher than those with Pauci-JRA or Oligo-JIA (p < 0.05 or 0.001). CONCLUSION: Small joint disease and wrist disease are suggestive of Oligo-JPsA. The use of a criterion consisting of small joint disease and/or wrist disease and/or dactylitis instead of dactylitis alone may increase the ability to differentiate Oligo-JPsA from Pauci-JRA or Oligo-JIA.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".