Hypertriglyceridemic waist and newly-diagnosed diabetes among remote-dwelling Indigenous Australians
Bibliographic record
Abstract
AIMS: Hypertriglyceridemic waist (HTgW) is predictive of cardiovascular disease. The HTgW relationship with diabetes is little studied. METHODS: This study analysed data from diabetes and cardiovascular risk factor screening programmes in remote Indigenous Australian settlements. Elevated waist girth (EW) was defined as ≥90 cm for men (n = 1134) or ≥80 cm for women (n = 1313). Hypertriglyceridemia (ETg) was defined as ≥1.7 mmol/L. Diabetes was defined as fasting plasma glucose ≥7.0 mmol/L. Body mass index (BMI) was categorised as <22, 22-24.9 and >25.0 kg/m(2). Logistic regression was used to analyse the odds of newly-diagnosed diabetes for individuals with either HTgW, ETg or EW, relative to individuals with values below cut-offs. RESULTS: The prevalence of HTgW was 33.2% for men and 34.8% for women. Accounting for age-group and gender, newly-diagnosed diabetes was associated (odds ratio (OR) (95% confidence interval)) with HTgW: 9.6 (6.6, 13.8). The relationship remained strong after accounting for the covariates BMI and smoking (OR = 4.9 (2.7, 8.8)). In BMI-stratified analyses the strongest odds were observed for the lowest category (<22 kg/m(2): OR = 12.9 (4.0, 41.7)). CONCLUSIONS: HTgW has a high prevalence and is associated with newly-diagnosed diabetes in Indigenous people, particularly those with BMI <22 kg/m(2), whom clinicians might not normally consider for screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".