Associations between Changes in Abdominal and Thigh Muscle Quantity and Quality
Bibliographic record
Abstract
BACKGROUND: Computed tomography (CT) images at L4-L5, T12-L1 and the midthigh are commonly used to assess abdominal adiposity, liver fat, and muscle quantity (mass) and quality (lipid), respectively. Unknown is whether abdominal skeletal muscle (SM) at L4-L5 or T12-L1 can also be used to reflect thigh SM quantity and quality, which is often considered the criterion measure. Also unclear is whether changes in thigh SM quantity and quality are reflected by corresponding changes in abdominal SM quantity and quality. PURPOSE: We examined the associations between abdominal SM and thigh SM quantity and quality in overweight/obese postmenopausal women before (n = 125) and after (n = 86) a 6-month exercise intervention trial. METHODS: CT was used to assess muscle quantity and quality at the midthigh, L4-L5, and T12-L1. RESULTS: At baseline, abdominal SM quality was significantly associated with thigh SM quality at L4-L5 and T12-L1 (R2 = 0.22 and R2 = 0.37, respectively; P < 0.01). Similarly, at baseline, abdominal SM quantity and quality was significantly associated with thigh SM quantity at L4-L5 and T12-L1 (R2 = 0.37 and R2 = 0.48, respectively; P < 0.01). Changes in thigh SM mass were marginally associated with changes in abdominal SM at T12-L1 (R2 = 0.08, P = 0.01) but not at L4-L5 (P > 0.10). Changes in thigh SM quality were associated with corresponding changes in abdominal SM quality at both L4-L5 and T12-L1 (R2 = 0.43 and R2 = 0.73, respectively). CONCLUSIONS: Measurements obtained from a single CT image taken within the abdomen are moderately correlated with measures of thigh SM quantity and quality at baseline. Changes in thigh SM mass are poorly tracked by changes in abdominal SM. However, abdominal SM does provide comparable measures of muscle quality change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".