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Record W2147391073 · doi:10.1016/s2213-8587(14)70137-8

Metabolic profiles and treatment gaps in young-onset type 2 diabetes in Asia (the JADE programme): a cross-sectional study of a prospective cohort

2014· article· en· W2147391073 on OpenAlexfundno aff
Roseanne O. Yeung, Yuying Zhang, Andrea O. Y. Luk, Wenying Yang, Leorino Sobrepeña, Kun‐Ho Yoon, S. R. Aravind, Wayne Huey‐Herng Sheu, Thy Khue Nguyen, Risa Ozaki, Chaicharn Deerochanawong, Chiu Chi Tsang, Wing-Bun Chan, Eun Gyoung Hong, Trung Quan, Yu Cheung, Nicola Brown, Su Yen Goh, Ronald C.W., M. Mukhopadhyay, Arvind Kumar Ojha, Shaibal Chakraborty, Alice P.S. Kong, Winnie Lau, Weiping Jia, Wenhui Li, Xiaohui Guo, Rongwen Bian, Jianping Weng, Linong Ji, Mercedes Reyes-dela Rosa, Ronaldo M Toledo, Thep Himathongkam, C. C. Chow, Larry L-T. Ho, Lee‐Ming Chuang, Greg Tutino, Peter C.Y. Tong, Wing‐Yee So, Troels Wolthers, Gary T.C. Ko, Greg Lyubomirsky, Juliana C.N. Chan

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2014
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
FundersUniversity of British ColumbiaUniversity of Melbourne
KeywordsMedicineDiabetes mellitusType 2 diabetesCohortCross-sectional studyYoung adultPopulationProspective cohort studyPediatricsJADE (particle detector)Age of onsetCohort studyInternal medicineDiseaseEnvironmental healthEndocrinologyPathology

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)0.001
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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

Citations302
Published2014
Admission routes1
Has abstractno

Explore more

Same venueThe Lancet Diabetes & EndocrinologySame topicDiabetes, Cardiovascular Risks, and LipoproteinsFrench-language works237,207