Validation and comparison of three formulae to estimate sodium and potassium excretion from a single morning fasting urine compared to 24-h measures in 11 countries
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although 24-h urinary measure to estimate sodium and potassium excretion is the gold standard, it is not practical for large studies. We compared estimates of 24-h sodium and potassium excretion from a single morning fasting urine (MFU) using three different formulae in healthy individuals. METHODS: We studied 1083 individuals aged 35-70 years from the general population in 11 countries. A 24-h urine and MFU specimen were obtained from each individual. A subset of 448 individuals repeated the measures after 30-90 days. The Kawasaki, Tanaka, and INTERSALT formulae were used to estimate urinary excretion from a MFU specimen. RESULTS: The intraclass correlation coefficient (ICC) between estimated and measured sodium excretion was higher with Kawasaki (0.71; 95% confidence interval, CI: 0.65-0.76) compared with INTERSALT (0.49; 95% CI: 0.29-0.62) and Tanaka (0.54; 95% CI: 0.42-0.62) formulae (P <0.001). For potassium, the ICC was higher with the Kawasaki (0.55; 95% CI: 0.31-0.69) than the Tanaka (0.36; 95% CI: -0.07 to 0.60; P <0.05) formula (no INTERSALT formula exists for potassium). The degree of bias (vs. the 24-h urine) for sodium was smaller with Kawasaki (+313 mg/day; 95% CI: +182 to +444) compared with INTERSALT (-872 mg/day; 95% CI: -728 to -1016) and Tanaka (-548 mg/day; 95% CI: -408 to -688) formulae (P <0.001 and P = 0.02, respectively). Similarly for potassium, the Kawasaki formula provided the best agreement and least bias. Blood pressure correlated most closely and similarly with the 24-h and Kawasaki estimates for sodium compared with the other two formulae. CONCLUSION: In a diverse population, the Kawasaki formula is the most valid and least biased method of estimating 24-h sodium excretion from a single MFU and is suitable for population studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it