A151: Pediatric Rheumatology Care and Outcomes Improvement Network Demonstrates Performance Improvement on Juvenile Idiopathic Arthritis Quality Measures
Bibliographic record
Abstract
Background/Purpose: Pediatric Rheumatology Care and Outcomes Improvement Network (PR‐COIN) is a multi‐site learning network designed to improve outcomes of juvenile idiopathic arthritis (JIA) care. Teams collect point of care data on measures of process of care and outcomes of care for the purposes of analysis to guide improvement activities. Eleven North American pediatric rheumatology centers participate. This report illustrates our improvement in several JIA process quality measures (QMs). Methods: Process of care QMs targeted for improvement include measurement of: arthritis‐related pain, physician global assessment, joint count, health‐related quality of life, physical function, as well as screening for uveitis, medication toxicity, and tuberculosis per guidelines. Outcome measures for JIA include clinical inactive disease, clinical remission on and off medications, no or mild pain level, and optimal physical functioning. Network goals were determined for each process and outcome measure. Data are collected with IRB approval and informed consent, and the shared registry for data entry is the ACR's Rheumatology Clinical Registry. Site‐specific and aggregate data are analyzed and displayed monthly via statistical process control charts allowing PR‐COIN to track performance over time. Individual centers use established quality improvement methodology to reach and exceed pre‐determined goals. Results: Data from 5112 encounters for 1134 JIA patients have been collected since April 2011. QMs with performance meeting or exceeding initial goals include documentation of complete joint count and measurement of arthritis‐related pain. For PR‐COIN network as a collective unit, QMs improved in six processes—measurement of functional ability, completion of ongoing medication toxicity labs, documentation of complete joint count, medication counseling for newly prescribed DMARDs, documentation of annual medication counseling, and measurement of health‐related quality of life. All of these measures had a shift above the baseline mean, demonstrating special cause. In addition, five sites have demonstrated individual improvement in at least one process QM. Conclusion: PR‐COIN sites are collectively and individually demonstrating significant improvements in JIA process of care QMs. Quality improvement efforts in PR‐COIN are ongoing with the goal of improving the outcome for patients with JIA.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".