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 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".