Association between Abdominal Fat (DXA) and Its Subcomponents (CT Scan) before and after Weight Loss in Obese Postmenopausal Women: A MONET Study
Bibliographic record
Abstract
Introduction. Subcutaneous fat (ScF) and visceral fat (VF) measurements using CT scan are expensive and may imply significant radiation doses. Cross-sectional studies using CT scan showed that ScF and VF are significantly correlated with abdominal fat measured by DXA (AF-DXA). The association has not been studied after a weight loss. Objective. To determine (1) the associations between AF-DXA and ScF and VF before and after weight loss and (2) the associations between their changes. Methods. 137 overweight/obese postmenopausal women were divided in two groups (1-caloric restriction or 2-caloric restriction + resistance training). AF was assessed using DXA and CT scan. Results. Correlations between AF-DXA and ScF (before: r = 0.87, after; r = 0.87; P < .01) and, AF-DXA and VF (before: r = 0.61, after; r = 0.69; P < .01) are not different before and after the weight loss. Correlations between delta AF-DXA and delta ScF (r = 0.72; P < .01) or delta VF (r = 0.51; P < .01) were found. Conclusion. The use of AF-DXA as a surrogate for VF after weight loss is questionable, but may be interesting for ScF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".