Incidence and prevalence of juvenile idiopathic arthritis in Catalonia (Spain)
Bibliographic record
Abstract
OBJECTIVE: To ascertain the incidence and prevalence of juvenile idiopathic arthritis (JIA) in Catalonia (autonomous region in northeast Spain), examined according to the currently established disease subtypes. METHODS: Before initiating the study, we conducted an educational programme on paediatric rheumatology, addressed to all general paediatricians in Catalonia. A 2-year (2004-2006), prospective, population-based study was then carried out to determine the incidence of JIA. Prospective and retrospective data retrieval was performed to calculate prevalence. The International League of Associations for Rheumatology (ILAR, Edmonton revision) classification criteria were applied. RESULTS: Over the study period, 145 new cases of JIA were diagnosed. The mean annual incidence was 6.9/10⁵ children aged less than 16 years (range 5.8-8.1 years; 9.0 years for girls and 4.8 years for boys). On separate analysis of patients ≤ 6 and > 6 years, the distribution in younger children was found to be similar for both girls and boys, whereas in older children, most girls belonged to the oligoarthritis and polyarthritis subgroups, and boys to the enthesitis-related arthritis and undifferentiated subgroups. The calculated prevalence of JIA (31 October 2006) was 39.7 (36.1-43.7)/10⁵ children younger than 16. The relative risk of girls having JIA was 2.1 [95% confidence interval (CI) 1.7-2.7, p < 0.001]. In 70% of patients, the diagnosis was established before the age of 7. Subgroup distribution of prevalent cases mirrored that of incident cases. CONCLUSION: This is the first population-based study on the epidemiology of JIA in Catalonia. Incidence and prevalence rates are lower than those reported for several areas in Nordic countries of Europe. Oligoarthritis was the most common subtype.
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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 | 0.001 |
| 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 | 0.000 | 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".