Research priorities in pediatric rheumatology: The Childhood Arthritis and Rheumatology Research Alliance (CARRA) consensus
Bibliographic record
Abstract
BACKGROUND: North American pediatric rheumatologists have created an investigator-initiated research network (the Childhood Arthritis and Rheumatology Research Alliance - CARRA) to facilitate multi-centre studies. One of the first projects undertaken by this network was to define, by consensus, research priorities for the group, and if possible a first group-sponsored clinical trial in which all members could participate. METHODS: We determined consensus using the Delphi approach. This approach has been used extensively in health research to reach consensus in large groups. It uses several successive iterations of surveys eliciting ideas and opinions from specialists in the field. Three surveys were designed based on this method and were distributed to members of CARRA to elicit and rank-order research priorities. RESULTS: A response rate of 87.6% was achieved in the final survey. The most highly ranked research suggestion was to study infliximab treatment of uveitis unresponsive to methotrexate. Other highly ranked suggestions were to study i) the treatment of systemic arthritis with anakinra and ii) the treatment of pediatric systemic lupus erythematosus with mycophenolate mofetil. CONCLUSION: The Delphi approach was an effective and practical method to define research priorities in this group. Ongoing discussion and cooperation among pediatric rheumatologists in CARRA and others world-wide will help in developing further research priorities and to facilitate the execution of clinical trials in the future.
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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 | 0.290 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".