Identifying Research Priorities among Patients and Families of Children with Rheumatic Diseases Living in the United States
Bibliographic record
Abstract
OBJECTIVE: To improve the quality and participation in pediatric rheumatology research, patient-prioritized studies should be emphasized. We collaborated with United States-based pediatric rheumatology advocacy organizations to survey patients and caregivers of children with rheumatic diseases to identify what research topics were most important to them. METHODS: We conducted Web-based surveys and focus groups (FG) of patients and caregivers of children with juvenile myositis (JM), juvenile arthritis (JA), and childhood-onset systemic lupus erythematosus (cSLE). Surveys were emailed to listservs and posted to social media sites of JM, JA, and cSLE patient advocacy organizations. An initial survey asked open-ended questions about patient/caregiver research preferences. Responses were further characterized through FG. A final ranking survey asked respondents to rank from a list of research themes the 7 most important to them. RESULTS: There were 365 JM respondents, 44 JA respondents, and 32 cSLE respondents to the final ranking survey. The top research priority for JM was finding new treatments, and for JA and cSLE, the priority was understanding genetic/environmental etiology. The 3 prioritized research themes common across all disease groups were medication side effects, disease flare, and disease etiology. CONCLUSION: Patient-centered research prioritization is recognized as valuable in conducting high-quality research, yet there is a paucity of data describing patient/family preferences, especially in pediatrics. We used multimodal methodologies to assess current patient/caregiver research priorities to help frame the agenda for the pediatric rheumatology research community. Patients and caregivers from all surveyed disease groups prioritized the study of medication side effects, disease flares, and disease etiology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".