Association Between Skeletal Muscle Mass Indices and Cognitive Function Among Inpatients With Stable Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To investigate the correlation between appendicular skeletal muscle mass (ASM)/height (ASMIht), ASM/body mass index (ASMIBMI), ASM/weight (ASMIwt), and ASM/waist circumference (ASMIwc) and cognitive function among inpatients with stable schizophrenia. METHODS: This was a cross-sectional study of 235 stable schizophrenia inpatients, including 60% males (n=141). Patient demographic information and body composition data were collected. The Montreal Cognitive Assessment-Chinese version (MoCA-C) was used to measure cognitive function. To determine the association between the muscle mass indices and cognitive function, multiple linear regressions were established. RESULTS: The median age of males and females were 51 years (range 42-55) and 51 (range 39-58), respectively. Spearman's correlation analysis revealed a significant association between ASMIwc and the MoCA-C scores (r=0.323, false discovery rate [FDR]=0.004) in males, while ASMIBMI, ASMIwt, and ASMIwc (r=0.268-0.421, all FDR <0.05) were significantly correlated with MoCA-C scores in females. Furthermore, covariate-adjusted multiple linear regression analysis further confirmed that only the ASMIwc was related to MoCAC scores after controlling for relevant variables (males: β=0.565, 95% confidence interval [CI], 0.156-0.974, p=0.007; females: β=0.96, 95% CI, 0.394-1.526, p=0.001). CONCLUSION: Our findings showed a substantial correlation between the ASMIwc and cognitive function in schizophrenia inpatients. Further validation of these data in broader study populations is now necessary.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".