The Childhood Arthritis and Rheumatology Research Alliance Consensus Treatment Plans
Bibliographic record
Abstract
The pediatric rheumatic diseases are a heterogeneous group of rare diseases, posing a number of challenges for the use of traditional clinical and translational research methods. Innovative comparative effectiveness approaches are needed to efficiently study treatment strategies and disease outcomes. The Childhood Arthritis and Rheumatology Research Alliance (CARRA) developed the consensus treatment plan (CTP) approach as a comparative effectiveness tool for research in pediatric rheumatology. CTPs are treatment strategies, developed by consensus methods among CARRA members, intended to reduce variation in treatment approaches, standardize outcome measurements, and allow for comparison of the effectiveness of different approaches with the goal of improving disease outcomes. To date, CTPs have been published for 8 different diseases and disease manifestations. The approach has been successfully piloted for juvenile localized scleroderma, systemic juvenile idiopathic arthritis (JIA), polyarticular JIA, dermatomyositis, and lupus nephritis. Large-scale studies are underway for systemic JIA and polyarticular JIA, with the CARRA patient registry serving as the data collection platform. These studies have been designed with stakeholder involvement, including active input from CARRA providers, patients, and parents, with the goal of increasing feasibility and ensuring the relevance of the outcomes. These studies include ancillary biologic specimen collection intended to support additional translational and mechanistic studies. Data from these ongoing CTP studies will provide more information on the ability of this approach to identify effective treatment strategies and improve outcomes in the pediatric rheumatic diseases.
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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 | 0.081 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".