The Relationship between Intramuscular Adipose Tissue, Functional Mobility, and Strength in Postmenopausal Women with and without Type 2 Diabetes
Bibliographic record
Abstract
Objectives. To determine (1) whether intramuscular adipose tissue (IntraMAT) differs between women with and without type 2 diabetes and (2) the association between IntraMAT and mobility and strength. Methods. 59 women ≥ 65 years with and without type 2 diabetes were included. A 1-Tesla MRI was used to acquire images of the leg. Timed-up-and-go (TUG) and grip strength were measured. Regression was used to determine associations between the following: (1) type 2 diabetes and IntraMAT (covariates: age, ethnicity, BMI, waist : hip ratio, and energy expenditure), (2) IntraMAT and TUG (covariates: diabetes, age, BMI, and energy expenditure), and (3) IntraMAT and grip strength (covariates: diabetes, age, height, and lean mass). Results. Women with diabetes had more IntraMAT. After adjustment, IntraMAT was similar between groups (diabetes mean [SD] = 13.2 [1.4]%, controls 11.8 [1.3]%, P = 0.515). IntraMAT was related to TUG and grip strength, but the relationships became nonsignificant after adjustment for covariates (difference/percent IntraMAT [95% CI]: TUG = 0.041 seconds [-0.079-0.161], P = 0.498, grip strength = -0.144 kg [-0.335-0.066], P = 0.175). Conclusions. IntraMAT alone may not be a clinically important predictor of functional mobility and strength; however, whether losses in functional mobility and strength are promoted by IntraMAT accumulation should be explored.
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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) | 0.001 | 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".