In vivo precision of the GE iDXA for the assessment of total body composition and fat distribution in severely obese patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate the precision of the iDXA for total body composition and fat distribution measurements in severely obese patients. DESIGN AND METHODS: Sixty-five severely obese participants with a mean age of 46 ± 11 years, BMI of 49 ± 6 kg/m(2) , and a mean body mass of 137.3 ± 20.9 kg took part in this investigation. Two consecutive iDXA scans with repositioning of the total body were conducted for each participant. The coefficient of variation (CV), the root-mean-square (RMS) averages of standard deviations of repeated measurements, the corresponding 95% least significant change, and Intraclass Correlations (ICC) were calculated. RESULTS: Precision expressed as % CV, for total body bone mineral content, fat free mass, total body fat, total body lean, and % total body fat were 1.08%, 0.94%, 0.90%, 1.00%, 0.79%, respectively. Precision was 1.44% for gynoid fat distribution and 1.64% for android fat (AF) distribution. The ICCs in all DXA measurements were 0.99 with % AF having the lowest at 0.96. CONCLUSIONS: The GE Lunar iDXA™ demonstrated excellent precision for total body composition assessments and is the first study to assess reproducibility in severely obese individuals.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".