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
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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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".