School Absence Among Children With Juvenile Idiopathic Arthritis: A National Matched Comparison Study
Bibliographic record
Abstract
OBJECTIVE: This study compares rates of school absence (SA) for all children with juvenile idiopathic arthritis (JIA) attending public Danish schools to peers both before and after JIA diagnosis. Further, we aimed to investigate the role of socioeconomic status (SES) on the possible association. METHODS: We performed a register-based matched cohort study. We included all children attending public Danish schools between 2010 and 2019 diagnosed with JIA, and compared them to their schoolmates. Rates of differentiated and total SA both before and after JIA diagnosis were compared. In the primary study we included children diagnosed with JIA after starting school, whereas the secondary study included only children diagnosed before starting school. RESULTS: We included 786 children with JIA and 3908 matched controls in the primary study and 382 children with JIA and 1910 matched controls in the secondary. Our primary study showed higher rates of sickness SA and total SA from 3 years before diagnosis and the following 5 years after diagnosis among children with JIA. After diagnosis, children with JIA also had significantly more legal (planned) SA. In the secondary study, we found that children diagnosed with JIA before starting school had significantly more SA (both sickness SA and legal school SA) up to grade 8. In both studies, children from low SES backgrounds, both with and without JIA, had the highest rates of SA, although no difference in the association between JIA and SA across SES groups was found. CONCLUSION: Children with JIA had more sickness and legal absence from school.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".