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Record W4321499505 · doi:10.1177/12034754231156103

Top Ten Research Priorities for Psoriasis, Atopic Dermatitis and Hidradenitis Suppurativa: The SkIN Canada Priority Setting Initiative

2023· article· en· W4321499505 on OpenAlexafffundabout
Aaron M. Drucker, Omer Kleiner, Rachael Manion, Anie Philip, Jan Dutz, Kathleen Barnard, Julie Fradette, Lucie Germain, Robert Gniadecki, Ivan V. Litvinov, Sarvesh Logsetty, Morris F. Manolson, P. Régine Mydlarski, Vincent Piguet, Debbie Ward, Youwen Zhou, An‐Wen Chan, Mariam Abbas, Raed Alhusayen, Lisa Cenedese, Tiffany Chen, Yee Sing Cheng, Trish Cole, Jacob De Iuliis, Katherine Desaulniers, Catherine Duffy, Tracy Ferris, Sameh Hanna, Rhiannon Humeny, Marissa Joseph, Ushra Khan, Charles Lynde, Steven Morrison, Boluwaji Ogunyemi, Vimal H. Prajapati, Michele Ramien, Lauren M. Reynolds, Cheryl F. Rosen, Kimberly Seguin, Cathryn Sibbald, Jennifer L. Swan, Jodi Timgren, Irina Turchin, Vicky Verner, Sandra A. Walsh, Veronica Weston

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsSickKids FoundationAlberta Children's HospitalMemorial University of NewfoundlandLynde Centre for DermatologySunnybrook HospitalHospital for Sick ChildrenUniversity of SaskatchewanUniversity of ManitobaUniversity of AlbertaUniversité LavalMcGill UniversityMcGill University Health CentreUniversity of CalgaryUniversity of TorontoCanadian Arthritis Patient AllianceToronto Western HospitalUniversity of British ColumbiaSKiN HealthWomen's College Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineHidradenitis suppurativaSkin cancerHealth careAtopic dermatitisPsoriasisDermatologyFamily medicineDiseaseCancerPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.201
metaresearch head score (Gemma)0.172
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.996

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2010.172
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0230.025
Science and technology studies0.0180.006
Scholarly communication0.0210.008
Open science0.0120.023
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.058
GPT teacher head0.336
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes3
Has abstractyes

Explore more

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