Validation and Assessment of Three Methods to Estimate 24-h Urinary Sodium Excretion from Spot Urine Samples in Chinese Adults
Bibliographic record
Abstract
24-h urinary sodium excretion is the gold standard for evaluating dietary sodium intake, but it is often not feasible in large epidemiological studies due to high participant burden and cost. Three methods--Kawasaki, INTERSALT, and Tanaka--have been proposed to estimate 24-h urinary sodium excretion from a spot urine sample, but these methods have not been validated in the general Chinese population. This aim of this study was to assess the validity of three methods for estimating 24-h urinary sodium excretion using spot urine samples against measured 24-h urinary sodium excretion in a Chinese sample population. Data are from a substudy of the Prospective Urban Rural Epidemiology (PURE) study that enrolled 120 participants aged 35 to 70 years and collected their morning fasting urine and 24-h urine specimens. Bias calculations (estimated values minus measured values) and Bland-Altman plots were used to assess the validity of the three estimation methods. 116 participants were included in the final analysis. Mean bias for the Kawasaki method was -740 mg/day (95% CI: -1219, 262 mg/day), and was the lowest among the three methods. Mean bias for the Tanaka method was -2305 mg/day (95% CI: -2735, 1875 mg/day). Mean bias for the INTERSALT method was -2797 mg/day (95% CI: -3245, 2349 mg/day), and was the highest of the three methods. Bland-Altman plots indicated that all three methods underestimated 24-h urinary sodium excretion. The Kawasaki, INTERSALT and Tanaka methods for estimation of 24-h urinary sodium excretion using spot urines all underestimated true 24-h urinary sodium excretion in this sample of Chinese adults. Among the three methods, the Kawasaki method was least biased, but was still relatively inaccurate. A more accurate method is needed to estimate the 24-h urinary sodium excretion from spot urine for assessment of dietary sodium intake in China.
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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 | 0.113 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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 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".