Precision of the iDXA for Visceral Adipose Tissue Measurement in Severely Obese Patients
Bibliographic record
Abstract
UNLABELLED: A new measurement tool, the automated software CoreScan, for the GE Lunar iDXA, has been validated for measuring visceral adipose tissue (VAT) against computed tomography in normal-weight populations. However, no study has evaluated the precision of CoreScan in measuring VAT among severely obese patients. PURPOSE: The purpose of the study was to evaluate the precision of CoreScan for VAT measurements in severely obese adults (body mass index > 40 kg·m(-2)). METHODS: A total of 55 obese participants with a mean age of 46 ± 11 yr, body mass index of 49 ± 6 kg·m(-2), and body mass of 137.3 ± 21.3 kg took part in this study. Two consecutive iDXA scans with repositioning of the total body were conducted for each participant. The coefficient of variation, the root-mean-square averages of SD of repeated measurements, the corresponding 95% least significant change, and intraclass correlations were calculated. RESULTS: Precision error was 8.77% (percent coefficient of variation), with a root-mean-square SD of 0.294 kg and an intraclass correlation of 0.96. Bland-Altman plots demonstrated a mean precision bias of -0.08 ± 0.41 kg, giving a coefficient of repeatability of 0.82 kg and a bias range of -0.890 to 0.725 kg. CONCLUSIONS: When interpreting VAT results with the iDXA in severely obese populations, clinicians should be aware of the precision error for this important clinical parameter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".