Outcome Monitoring and Clinical Decision Support in Polyarticular Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Inconsistent assessment and treatment may impair juvenile idiopathic arthritis (JIA) outcomes. We aimed to improve polyarticular JIA (rheumatoid factor-positive and -negative) outcomes by standardizing point-of-care disease activity monitoring and implementing clinical decision support (CDS) to reduce treatment variation. METHODS: We performed a quality improvement initiative in an outpatient pediatric rheumatology practice. The interventions, implemented from April to November 2016, included standardized disease activity measurement, disease activity target review, and phased introduction of polyarticular JIA CDS to guide medication selection, dosing, treatment duration, and tapering. Process measures included visit-level target attestation (goal: 50%) and CDS use (goal: 15%). Our goal was to reduce the polyarticular JIA clinical Juvenile Arthritis Disease Activity Score (cJADAS-10) by at least 10%. Included patients had at least 2 visits from April 2016 through July 2017, and were classified as having early (≤ 6 mos) or established disease (> 6 mos). RESULTS: Patients with polyarticular JIA (n = 97; 81% established disease) were observed for 10.3 months (interquartile range: 6.4-12.3). Target attestation and CDS use occurred in a mean of 77% and 45% of polyarticular JIA visits, respectively. The median cJADAS-10 decreased significantly in both early (16.5 to 2.7, p < 0.001) and established polyarticular JIA (2.1 to 1.0, p = 0.01). A high proportion of patients with early disease received biologic therapy (73.7%). In established disease, although prescription of nonbiologic and biologic disease-modifying antirheumatic drugs remained similar overall, adalimumab prescribing increased (12.8% to 23.1%, p = 0.008). CONCLUSION: Implementation of structured disease activity monitoring and CDS in polyarticular JIA was associated with significant reductions in disease activity scores in both early and established disease.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".