Development and validation of the Kids Disability Screen for children with juvenile idiopathic arthritis: results from the CAPRI Registry
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to develop and validate a brief disability screen for children with JIA, the Kids Disability Screen (KDS). METHODS: A total of 216 children enrolled in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry in 2017-2018 formed a development cohort, and 220 children enrolled in 2019-2020 formed a validation cohort. At every clinic visit, parents answered two questions derived from the Childhood Health Assessment Questionnaire (CHAQ): 'Is it hard for your child to run and play BECAUSE OF ARTHRITIS?' ('Hard' 0-10), and 'Does your child usually need help from you or another person BECAUSE OF ARTHRITIS?' ('Help', 0-10). We used 36-fold cross-validation and tested nine different mathematical methods to combine the answers and optimize psychometric properties. The results were confirmed in the validation cohort. RESULTS: Expressed as the mean of the two answers, KDS best balanced ease of use and psychometric properties, while a LASSO regression model combining the two answers with other patient characteristics [estimated CHAQ [eCHAQ]) had the highest responsiveness. In the validation cohort, 22.7%, 25.9% and 28.6% of patients had a score of 0 at enrolment for the KDS, eCHAQ and CHAQ, respectively. Responsiveness was 0.67, 0.74 and 0.62, respectively. Sensitivity to detect a CHAQ > 0 was 0.90 and specificity 0.56, KDS detecting some disability in 44% of children with a CHAQ = 0. CONCLUSION: This simple KDS has psychometric properties comparable with those of a full CHAQ and may be used at every clinic visit to identify those children who need a full disability assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".