Defining patient‐centered research priorities in pediatric dermatology
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Patient and caregiver perspectives are critical in understanding dermatologic disease impact, presentation, and management in children. The Pediatric Dermatology Research Alliance (PeDRA) Patient Advisory Committee (PtAC), a group of patient representatives and parents of children with cutaneous disease, pursued a multistep, iterative, consensus-building process to identify comprehensive, high-priority research needs. METHODS: Building on discussions at the 2020 PeDRA Annual Conference, a research prioritization survey was developed and completed by PtAC members. Survey themes were aggregated and workshopped by the PtAC through a series of facilitated calls. Emerging priorities were refined in collaboration with additional PeDRA patient community members at the 2021 PeDRA Annual Conference. Subsequently, a final actionable list was agreed upon. RESULTS: Fourteen PtAC members (86.7% female) representing patients with alopecia areata, atopic dermatitis, vascular birthmarks, congenital melanocytic nevi, ectodermal dysplasias, epidermolysis bullosa, Gorlin syndrome, hidradenitis suppurativa, ichthyosis, pemphigus, psoriasis, Sturge-Weber syndrome, and pachyonychia congenita completed the survey. Following serial PtAC meetings, 60 research needs were identified from five domains: psychosocial challenges, health care navigation/disease management, causes/triggers, treatments to preserve or save life, and treatments to preserve or save quality of life. CONCLUSIONS: Many pediatric dermatology research priorities align across affected communities and may drive meaningful, patient-centric initiatives and investigations.
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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 | 0.360 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".