Interindividual variation in abdominal subcutaneous and visceral adipose tissue: influence of measurement site
Bibliographic record
Abstract
We evaluated the influence of measurement site on the ranking (low to high) of abdominal subcutaneous (SAT) and visceral (VAT) adipose tissue. We also determined the influence of measurement site on the prediction of abdominal SAT and VAT mass. The subjects included 100 men with computed tomography (CT) measurements at L4-L5 and L3-L4 levels and 100 men with magnetic resonance imaging (MRI) measurements at L4-L5 and 5 cm above L4-L5 (L4-L5 +5 cm). Corresponding mass values were determined by using multiple-image protocols. For SAT, 90 and 92 of the 100 subjects for CT and MRI, respectively, had a difference in rank position at the two levels. The change in rank position exceeded the error or measurement for approximately 75% of the subjects for both methods. For VAT, 91 and 95 of the 100 subjects for CT and MRI, respectively, had a difference in rank position at the two levels. The change in rank position exceeded the error of measurement for 36% of the subjects for CT and for 8% of the subjects for MRI. For both imaging modalities, the variance explained in SAT and VAT mass (kg) was comparable for L4-L5, L4-L5 +5 cm, and L3-L4 levels. In conclusion, the ranking of subjects for abdominal SAT and VAT quantity is influenced by measurement location. However, the ability to predict SAT and VAT mass by using single images obtained at the L4-L5, L4-L5 +5 cm, or L3-L4 levels is comparable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".