Correlation Analysis of Serum Uric Acid and Uric Acid Creatinine Ratio With Sarcopenia in the Elderly
Bibliographic record
Abstract
Objectives: Sarcopenia is a progressive and systemic skeletal muscle disease. Uric acid is a powerful endogenous antioxidant and an indicator reflecting the nutritional status in the human body. Serum uric acid creatinine ratio (UCR) is serum uric acid (SUA) corrected by renal function. The relationship between SUA, UCR, and sarcopenia remains underexplored. This study explored the correlation between SUA, UCR, and sarcopenia in elderly patients. Methods: test, or chi-squared test was used to compare the differences between groups. Spearman correlation analysis was used to analyze the correlation between SUA, UCR, and skeletal muscle mass index (SMI) and handgrip strength. The relationship between SUA, UCR, and sarcopenia was estimated by a multivariate logistic regression model. ROC curve was drawn to test the diagnostic efficacy of SUA and UCR for sarcopenia. Results: The levels of SUA and UCR were significantly lower in participants with sarcopenia. Spearman correlation analysis showed that SUA and UCR were positively correlated with handgrip strength and skeletal muscle mass index. Multivariate logistic regression analysis showed that, after adjusting for relevant confounding factors, UCR remained significantly associated with sarcopenia, while SUA didn't. The AUC of SUA combined with UCR in diagnosing sarcopenia in males was 0.744. In females, the progressive significance of SUA was not statistically significant. The AUC of UCR was 0.658. Conclusion: In the elderly, SUA and UCR are related to sarcopenia, but there are certain gender differences.
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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 | 0.005 |
| 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 | 0.000 | 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".