Obstacles in Early Diagnosis of Children With Juvenile Idiopathic Arthritis: A Nationwide Israeli Retrospective Study
Bibliographic record
Abstract
Objective Characterization of the stages that patients with juvenile idiopathic arthritis (JIA) pass until they are diagnosed, and analysis of the different causes that lead to a delay in JIA diagnosis in Israel. Methods This is a retrospective cohort study conducted in 8 pediatric rheumatology centers in Israel. All patients diagnosed with JIA between October 2017 and October 2019 were included in the study. Demographic, clinical, and data regarding the referring physicians were collected from hospital and community medical charts. Results Of 207 patients included in the study, 201 cases were analyzed, 71.1% of the population were female. Patients, on average, were evaluated during the diagnostic process by 3.1 different physicians. In most cases, they initially met with a pediatrician in the community setting (61.2%), and later, most commonly referred to a rheumatologist by the community pediatrician (27.9%). The median time until diagnosis was 56.0 days (range: 1.0-2451.0 days). Patients diagnosed with polyarticular and spondyloarthritis/enthesitis-related arthritis (SpA/ERA) JIA subtypes had the longest period until diagnosis (median: 115.5 and 112.0 days, respectively). Younger age correlated with a quicker diagnosis, and females were diagnosed earlier compared to males. Fever at presentation significantly shortened the time to diagnosis (P< 0.01), whereas involvement of the small joints/sacroiliac joints significantly lengthened the time (P< 0.05). Conclusion This is the first nationwide multicenter study that analyzes obstacles in the diagnosis of JIA in Israel. Raising awareness about JIA, especially for patients with SpA/ERA, is crucial in order to avoid delays in diagnosis and treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".