“There’s so much to be done”: a qualitative study to elucidate research priorities in childhood-onset systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: There is a pressing need for high-quality, comprehensive research to describe the natural history, best treatments, access to care and disparities in care for patients with childhood-onset SLE (cSLE). Building on a previously published survey study of cSLE clinicians and researchers to describe research priorities in cSLE, the primary objective of this study was to conduct expert interviews to define high-priority areas for cSLE research. METHODS: Individuals with identified multidisciplinary expertise in cSLE were recruited worldwide using purposive sampling technique. Experts participated in open-ended, semistructured qualitative interviews. Interviews were designed to elicit expert perspectives on research priorities, optimal research approaches, and factors that facilitate and hinder advancing cSLE research. Interviews were digitally recorded, transcribed and de-identified for analysis. Analysis for underlying themes of cSLE expert perspectives was performed using a constant comparative approach. RESULTS: Twenty-nine experts with diverse clinical and research backgrounds participated. Themes emerged within five domains: (1) expanding disease knowledge; (2) investigator collaboration; (3) partnering with patients and families; (4) improving care to optimise research; and (5) overcoming investigator barriers. Choosing a singular area of focus was difficult; experts identified many competing priorities. Despite the numerous priorities that emerged, experts described several existing and potential opportunities for advancing cSLE research. CONCLUSIONS: In addition to the priorities identified by cSLE experts in this study, the opportunities for advancing cSLE research and care that were proposed should be used as a foundation for creation of a cSLE research agenda for both research and funding allocation.
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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 | 0.041 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".