Top Ten Research Priorities for Psoriasis, Atopic Dermatitis and Hidradenitis Suppurativa: The SkIN Canada Priority Setting Initiative
Bibliographic record
Abstract
BACKGROUND: The Skin Investigation Network of Canada (SkIN Canada) is a new national skin research network. To shape the research landscape and ensure its value to patient care, research priorities that are important to patients, caregivers, and health care providers must be identified. OBJECTIVES: To identify the Top Ten research priorities for 9 key skin conditions. METHODS: We first surveyed health care providers and researchers to select the top skin conditions for future research within the categories of inflammatory skin disease, skin cancers (other than melanoma), and wound healing. For those selected skin conditions, we conducted scoping reviews to identify previous priority setting exercises. We combined the results of those scoping reviews with a survey of patients, health care providers, and researchers to generate lists of knowledge gaps for each condition. We then surveyed patients and health care providers to create preliminary rankings to prioritize those knowledge gaps. Finally, we conducted workshops of patients and health care providers to create the final Top Ten lists of research priorities for each condition. RESULTS: Overall, 538 patients, health care providers, and researchers participated in at least one survey or workshop. Psoriasis, atopic dermatitis and hidradenitis suppurativa (inflammatory skin disease); chronic wounds, burns and scars (wound healing); and basal cell, squamous cell and Merkel cell carcinoma (skin cancer) were selected as priority skin conditions. Top Ten lists of knowledge gaps for inflammatory skin conditions encompassed a range of issues relevant to patient care, including questions on pathogenesis, prevention, non-pharmacologic and pharmacologic management. CONCLUSIONS: Research priorities derived from patients and health care providers should be used to guide multidisciplinary research networks, funders, and policymakers in Canada and internationally.
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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 | 0.201 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.012 | 0.023 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".