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Record W4296163277 · doi:10.1016/s2214-109x(22)00356-4

One hundred years of solitude—underrepresentation of Indigenous and minority groups in diabetes trials

2022· article· en· W4296163277 on OpenAlexaffabout
Shohinee Sarma, Lisa Richardson, John Neary

Bibliographic record

VenueThe Lancet Global Health · 2022
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsSt. Joseph’s Healthcare HamiltonUniversity Health NetworkMount Sinai HospitalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineDiabetes mellitusPopulationGestational diabetesEthnic groupType 2 diabetesIndigenousPregnancyGerontologyDemographyEnvironmental healthEndocrinologyGestation

Abstract

fetched live from OpenAlex

100 years after the discovery of insulin in Toronto, Indigenous people in Canada face the highest risk of developing diabetes, but are not well represented in diabetes research. Indigenous people have 3·5 times the age-standardised prevalence rate of diabetes and suffer complications such as end-stage renal disease twice as frequently compared to the general Canadian population.1 Indigenous women face higher gestational diabetes rates, highlighting the intergenerational impact of diabetes.2

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)0.059
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.092
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.108
GPT teacher head0.420
Teacher spread0.312 · 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.

Study designObservational
DomainMethods
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

Citations36
Published2022
Admission routes2
Has abstractyes

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