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 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 | 0.036 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".