Study design of the Multicultural Community Health Assessment Trial (M-CHAT): a comparison of body fat distribution in four distinct populations.
Bibliographic record
Abstract
OBJECTIVE: To outline the study design of the Multicultural Community Health Assessment Trial (M-CHAT). The purpose of the study is to compare the relationship between visceral adipose tissue (VAT) and total body fat in men and women of Aboriginal, Chinese, and South Asian origin with a similar group of men and women of European origin. DESIGN: A total of 200 apparently healthy men and women between the ages of 30 and 65 will be recruited from each of the local Aboriginal, Chinese, and South Asian and European communities. Within each sex/ethnic group, an equal representation of participants will have a body mass index between 18.5 to 24.9, 25 to 29.9 and >30. Each participant will undergo an assessment for VAT, total body fat, metabolic risk factors, physical activity, diet, quality of life, and sociodemographics. MAIN OUTCOME MEASURES: The primary outcome of this study is the relationship between VAT and total body fat in each of the Aboriginal, Chinese, and South Asian cohorts; this relationship will be compared to the European cohort after adjustment for age, sex, socioeconomic status, smoking status, physical activity, diet, and body mass index. CONCLUSIONS: This study will be the first to identify differences in body fat distribution in these populations. We anticipate that in populations of Aboriginal, Chinese, and South Asian origin, a greater proportion of total body fat will be deposited as VAT compared to those of European origin.
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 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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".