Later-onset rheumatoid factor negative polyarticular juvenile idiopathic arthritis (JIA): a unique patient group?
Bibliographic record
Abstract
OBJECTIVES: To determine the two-year outcome of patients with later-onset polyarticular rheumatoid factor (RF) negative (-) juvenile idiopathic arthritis (JIA), and predictors of outcome. METHODS: All patients ages 10 to16 years diagnosed and followed in the Rheumatology Clinic at SickKids Hospital with the diagnosis of polyarticular RF- JIA were eligible for study. A retrospective chart analysis was performed and number of active joints, medications, laboratory information and childhood health assessment questionnaire scores were recorded at diagnosis, and 6, 12, and 24 months following diagnosis. RESULTS: As early as 6 months after diagnosis the mean number of active joints decreased from 16 to < 10, with 50% of the patients having < 5 active joints. The predominant joints affected were the wrist, knee, and small joints of the hand. The only predictor of active joint count at the 2-year follow-up was initial presenting active joint count as classified as mild, moderate, or severe. Sex, age, and laboratory results at presentation did not show any correlation with active joint count at 2 years. Majority of patients were treated with non-steroidal anti-inflammatory drugs (98%) and at least one disease-modifying anti-rheumatic drug (56%). CONCLUSIONS: The two-year outcome of patients with late-onset RF- polyarticular JIA was very good with the majority of patients having minimally active disease at last follow-up. Presence of significant polyarthritis at presentation was the only feature associated with long-term joint activity. Sex and lab results did not show any correlation with active joint in this cohort of RF-JIA patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".