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Record W2188622236 · doi:10.1159/000497716

Gender associated differences in diet and anthropometric measures in Cape Breton Caucasians with well-controlled type 2 diabetes

2019· article· en· W2188622236 on OpenAlexaff
Kazimiera A. Mizier-Barre, Odette Griscti

Bibliographic record

VenueInternational Journal of Diabetes and Metabolism · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsCape Breton University
Fundersnot available
KeywordsWaistAnthropometryMedicineType 2 diabetesCalorieDemographyWaist–hip ratioPopulationCircumferenceDiabetes mellitusInternal medicineBody mass indexEndocrinologyEnvironmental health

Abstract

fetched live from OpenAlex

Diet and anthropometic measures are important indicators of risk of various complications in type 2 diabetics. The purpose was to assess the hypothesis that there would be gender differences in anthropometric measures and dietary intakes for energy and energy-yielding nutrients including oleic acid in the study population of well-controlled (HbA1c < 8 %) Caucasian type 2 diabetics. Males (n=18) and females (n = 14) participated. Subjects came in for two visits each 3 months apart. Males differed from females only in height (m) (1.72 + 0.02 vs 1.59 ± 0.01), hip circumference (cm) (103.0 ± 1.4 vs 117.8 ± 3.9), waist to hip ratio (0.99 ± 0.01 vs 0.87 ± 0.01 and waist to height ratio (0.591 ± 0.010 vs 0.640 ± 0.018). Despite a higher intake for total calories in males and similar intakes for the energy yielding nutrients including oleic acid in the study population of type 2 diabetics, males and females had similar weights and waist circumferences while males had greater waist to hip ratios, lesser waist to height ratios and very a strong trend toward lesser BMI. Females are further away from recommended anthropometric targets and as such appear to be at greater risk of complications from type 2 diabetes.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.991
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.252
Teacher spread0.238 · 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
Published2019
Admission routes1
Has abstractyes

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