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Record W7117585676 · doi:10.1177/12034754251408404

Streamlining Pediatric Atopic Dermatitis Care Across Inuit Nunangat: Cost Savings and In-Community IL-4/IL-13 Biologic Treatment Initiation

2025· article· en· W7117585676 on OpenAlexafffundabout
Meghan Kanou, Sabrina Hundal, Naomy Leclerc, Johnny Hanna, Annie Kumarluk, Nathalie Boulanger, Elena Netchiporouk, Richard Chamberland, Justin Grenier, Ivan V. Litvinov

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsNunavik Regional Board of Health and Social ServicesUniversité de MontréalUniversité LavalMcGill University Health CentreMcGill UniversityUniversity of Calgary
FundersFonds de Recherche du Québec - SantéInstitute of Indigenous Peoples' HealthLEO FondetCanadian Institutes of Health ResearchRéseau de cancérologie Rossy
KeywordsAtopic dermatitisMEDLINECost–benefit analysisHealth careCost analysis

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.931

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.025
GPT teacher head0.311
Teacher spread0.285 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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

Citations0
Published2025
Admission routes3
Has abstractno

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Same venueJournal of Cutaneous Medicine and Surgery→Same topicDermatology and Skin Diseases→French-language works237,207→