Ethnic differences in adiposity and body composition: the First Nations bone health study
Bibliographic record
Abstract
The purpose of this study was to compare patterns of adiposity and soft tissue composition in First Nations and white Canadian women. A population-based cross-sectional study was performed. A random age-stratified sample of 206 urban First Nations women and 177 white women was recruited. Soft tissue composition was analyzed with dual-energy X-ray absorptiometry. Analysis of covariance (ANCOVA) models were used to assess ethnicity in models that adjusted for body mass, body mass index (BMI), and socio-demographic factors. Obesity (BMI>or=30.0 kg/m2) was more common in First Nations women (48.1%) than in white women (36.2%, Fisher's exact test p=0.012). Mean trunk fat tissue mass fraction and total body fat mass fraction (as a percent of soft tissue) were greater in First Nations women than in white women (p<0.0001). Trunk lean tissue was also greater in First Nations women (p=0.027), but total body lean tissue was similar. The mean trunk adiposity index was strongly related to ethnicity (First Nations +0.5%+/-2.5% versus white -1.7%+/-2.6%, p<0.0001). Preferential fat accumulation in the trunk of First Nations women persisted after adjustment for body mass, BMI, and other socio-demographic variables (p<0.0001). First Nations women differ from white women in terms of fat and lean tissue mass and distribution. First Nations women had a preferential increase in trunk fat and this may contribute to high reported rates of diabetes, metabolic syndrome, and cardiovascular events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".