Estimation of Body Fat Mass Using Dual‐Energy X‐Ray Absorptiometry, Bioelectric Impedance Analysis, and Anthropometry in HIV‐Positive Male Subjects Receiving Highly Active Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: The purpose of this pilot study was to compare estimates of percentage body fat mass (FM) by bioelectric impedance analysis (BIA) and skinfold measurements (SF) with estimates obtained from dual-energy x-ray absorptiometry (DEXA) in 47 HIV-infected male subjects receiving highly active antiretroviral therapy (HAART). As different patterns of abdominal fat accumulation might affect the body FM estimation, correlation and agreement of these methods were also compared in patients with waist to hip ratio (WHR) < or =0.9 and >0.9. METHODS: Body FM was estimated by BIA and by measuring skinfold thickness at biceps, triceps, and subscapular area, and was compared with DEXA as the reference method using paired t-test. RESULTS: Estimates by SF were significantly higher and by BIA were significantly lower compared with DEXA for all subjects. This relationship persisted only in those with WHR >0.9. Both BIA and SF correlated significantly with DEXA, but they did not agree. However, both techniques showed a small intermethod bias, and the precision was within the acceptable range. This relationship persisted in those with WHR >0.9. In comparison with measurement by BIA, SF showed poorer agreement (larger bias and error). CONCLUSION: For population studies and perhaps to monitor changes over time for intervention studies, the bias for both BIA and SF methods is relatively small and errors and precisions are within the acceptable range when compared with DEXA, and thus all 3 techniques can be used for routine monitoring of total body FM in male subjects with HIV infection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".