Diagnostic Accuracy of Single Spot Urine for Detecting Renal Uric Acid Underexcretion in Men
Bibliographic record
Abstract
BACKGROUND: The uric acid (UA) clearance test to evaluate the hyperuricemia phenotype requires a great deal of time. However, the utility of single spot urine is scarce. The study aimed to determine the diagnostic accuracy of single spot urine for predicting renal UA underexcretion (the decreased UA excretion) in men. METHODS: A total of 73 male participants aged 20 - 74 years with a UA level of 6.0 - 7.9 mg/dL were enrolled in the study. Renal UA underexcretion was defined as < 7.3 mL/min using the 60-min method. Urinary UA to creatinine ratio (UACR), fractional clearance of urate (FCU), and the Simkin index were calculated. A receiver operating characteristic (ROC) analysis was performed to compare the diagnostic utility of these parameters for predicting UA underexcretion. RESULTS: In the ROC analysis, the area under the curve values of the UACR, FCU, and the Simkin index for predicting UA underexcretion were 0.903 (95% confidence interval (CI): 0.830 - 0.976), 0.841 (95% CI: 0.749 - 0.933), and 0.779 (95% CI: 0.673 - 0.885), respectively. An optimal UACR cutoff of 0.460 (sensitivity 89.2%, specificity 80.6%, overall diagnostic accuracy 84.9%, positive predictive value 82.5%, and negative predictive value 87.9%) was identified. CONCLUSIONS: These results suggest that the UACR is a simple and efficient test with high sensitivity and specificity levels for predicting renal UA underexcretion in men.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".