Concordance of BAI and BMI with DXA in the Newfoundland Population
Bibliographic record
Abstract
BACKGROUND: Body adiposity index (BAI), indirect method proposed to predict adiposity, was developed using Mexican Americans and very little data are available regarding its validation in Caucasian populations to date. OBJECTIVE: The study objectives were to validate the BAI with dual-energy X-ray absorptiometry (DXA) body fat percentage (%BF), taking into consideration the gender and adiposity status. DESIGN AND METHODS: A total of 2,601 subjects (Male 662, Female 1939) from our Complex Diseases in the Newfoundland population: Environment and Genetics (CODING) study participated in this investigation. Pearson correlations, with the entire cohort along with men and women separately, were used to compare the correlation of both BAI and BMI with %BF. Additionally, the concordance between BAI and BMI with %BF were also performed among normal-weight (NW), overweight (OW), and obese (OB) groups. Adiposity status was determined by the Bray Criteria according to DXA %BF. RESULTS: BAI performs better than BMI in our Caucasian population by: (1) reflecting the gender difference in total %BF between women and men, (2) correlating better with DXA %BF than BMI when women and men are combined, and (3) performing better in NW and OW subjects for both the sexes. However, BAI performs less effectively than BMI in OB men and women. CONCLUSION: In summary, the BAI method is a better estimate of adiposity than BMI in non-OB subjects in our Caucasian population. A measurement sensitive to the changes in adiposity for both men and women is suggested to be incorporated into the present BAI equation to increase accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".