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Record W2803910492 · doi:10.1093/pch/pxy054.007

LET’S MAKE A DIFFERENCE: EARLY SCREENING FOR PREDIABETES AND TYPE 2 DIABETES IN CANADIAN ABORIGINAL YOUTH

2018· article· en· W2803910492 on OpenAlexaffabout
Shelley Spurr, Jill Bally, Carol Bullin, Krista Trinder

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPrediabetesType 2 diabetesMedicineDiabetes mellitusPediatricsDemographyGerontologyEndocrinologySociology

Abstract

fetched live from OpenAlex

Type 2 diabetes (T2D) is one of the fastest growing paediatric chronic diseases worldwide.1 Canadian Aboriginal children are disproportionately affected by T2D, with an incidence rate estimated at 1.54 per 100,000 children per year.2 Recent research found that Aboriginal youth with diabetes experience higher risk for early complications and premature death than non-Aboriginal.3 These authors argued that young people with diabetes have a prolonged exposure to the metabolic consequences of this disease. Although a number of studies have examined the projected incidence rates and potential risk factors for T2D in the adolescent population, the true prevalence rate of prediabetes and T2D is underestimated specifically in the Aboriginal adolescent cohort. To our knowledge, this is the first population-based investigation into the prevalence of prediabetes in Canadian Aboriginal adolescents in the last decade. To investigate the risk factors and prevalence rates of prediabetes and type 2 diabetes among adolescents living in northern Canadian communities of which the majority were of Aboriginal decent. In this novel quantitative study, 160 high school students (aged 13–20) were recruited from three northern, pre-dominantly Canadian Aboriginal communities and were screened for risk for prediabetes and type 2 diabetes. Screening included demographic data, family history, anthropometrical measurements, blood pressure, and A1C. Descriptive and inferential statistics were computed using the Statistical Package for Social Sciences (SPSS v.22.0). Further, chi-square analyses were conducted to investigate if the risk factors of hypertension and obesity occurred at higher frequencies for males and females who presented with an increased HbA1c level. At least half of the adolescents presented with multiple risk factors for type 2 diabetes including Aboriginal ancestry, family history, overweight/obesity, and hypertension. In this sample, 10% had an A1C greater than 5.7%, 22.5% were overweight and 17.5% were obese, and 26.6% had hypertension or prehypertension. Further analysis showed that the 50% of the participants with an elevated HbA1c were also overweight/obese and 31% were prehypertensive/hypertensive. Of the females who were prediabetic, 14% were overweight and 43% were obese. For the males who presented with prediabetes, 22% were overweight and 22% were obese. In addition, 43% of females with an elevated HbA1c and who were classified as prediabetic were hypertensive and 14% were prehypertensive. In comparison, only 11% of the males who were classified as prediabetic were hypertensive and none were prehypertensive (Figure 1). Prediabetes is a growing health concern for young Aboriginal Canadians and there is an urgent need for early screening of both prediabetes and type 2 diabetes. To enhance positive health outcomes, interventions that are specific to the modifiable risk factors including overweight/obesity and hypertension are suggested. This has the potential to prevent the progression to diabetes and reduce related complications.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.015
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.313
Teacher spread0.289 · 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
Published2018
Admission routes2
Has abstractyes

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