Parent and Child Acceptable Symptom State in Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To explore the parent and child acceptable symptom state in juvenile arthritis (JA-PASS and JA-CASS, respectively) and estimate the JA-PASS and JA-CASS cutoff values for outcome measures. METHODS: Children with juvenile idiopathic arthritis (JIA) and their parents completed a multi-dimensional questionnaire that included parent-reported and child-reported outcomes and a question about whether they considered the disease state as satisfactory. Additional assessments included demographic data, physician-reported outcomes, and acute-phase reactant levels. Stepwise logistic regression was used to assess contributors to JA-PASS and JA-CASS. Cutoff values of outcome measures that defined JA-PASS and JA-CASS were determined using both 75th percentile and receiver-operating characteristic (ROC) curve methods. Testing procedures included evaluation of discriminative and construct validity of the satisfaction question and assessment of reliability of JA-PASS and JA-PASS cutoffs. RESULTS: Of 584 parents, 385 (65.9%) considered their child in JA-PASS. Of 343 children, 236 (68.8%) considered themselves in JA-CASS. Significant contributors to being in either JA-PASS or JA-CASS were absence of active joints, better rating of overall well-being, and better physical function or health. Cutoff values yielded by 75th percentile and ROC curve methods were similar. Parent, child, and physician global ratings yielded the lowest percentage of false-positive misclassification and the best tradeoff between sensitivity and specificity. The satisfaction question showed good discriminative and construct validity and the JA-PASS and JA-PASS cutoffs were found to be stable over time. CONCLUSION: The acceptable symptom state is a relevant concept for children with JIA and their parents and constitutes a valid outcome measure that is potentially applicable in routine practice and clinical trials.
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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 | 0.003 | 0.012 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".