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Record W4414422276 · doi:10.1016/j.jcjd.2025.09.002

Identifying Potential Sustainable and Scalable Interventions to Recognize Signs of Diabetes in Children Across Canada

2025· article· en· W4414422276 on OpenAlexafffundvenueabout
Zeenat Ladak, Geneviève Rouleau, Jennifer Shuldiner, Shazhan Amed, Elizabeth Cummings, Manpreet Doulla, Josephine Ho, Mark D. Inman, Sarah Lawrence, Patricia Li, Elizabeth Moreau, Meranda Nakhla, Julia von Oettingen, Elizabeth Sellers, Diane K. Wherrett, Rayzel Shulman, Celia Laur

Bibliographic record

VenueCanadian Journal of Diabetes · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsSickKids FoundationDiabetes CanadaCanadian Paediatric SocietyChildren's Hospital of Eastern OntarioMcGill University Health CentreUniversity of OttawaUniversity of CalgaryHospital for Sick ChildrenUniversity of SaskatchewanUniversity of AlbertaChildren's Hospital Research Institute of ManitobaWomen's College HospitalUniversité du Québec en OutaouaisNova Scotia Health AuthorityInstitut du Savoir MontfortMontfort HospitalDalhousie UniversityBC Children's HospitalMontreal Children's HospitalUniversity of Toronto
FundersCanadian Institutes of Health ResearchDiabetes Action CanadaSickkids Research InstituteNovo NordiskSociété Canadienne de Pédiatrie
KeywordsPsychological interventionDiabetes mellitusWork (physics)MEDLINE

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)0.002
metaresearch head score (Gemma)0.006
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 score0.074
Threshold uncertainty score0.540

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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 routes4
Has abstractno

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