Development of a Method for Estimating Dietary Salt Intake Using the Overnight Urinary Sodium/Potassium Ratio
Bibliographic record
Abstract
Background: There are many patients who need to restrict and assess salt in their diet. However, it is difficult to estimate daily salt intake accurately and easily. Therefore, a method for estimating dietary salt intake using the overnight urinary sodium (Na)/potassium (K) ratio was developed. Methods: The study involved 43 healthy adults (13 males, 30 females). From 2018 to 2020, subjects consumed a salt-adjusted diet for 11 - 30 days continuously, and overnight urine was collected daily. Using the previous day’s salt intake as the objective variable, an equation for estimating the salt intake was developed using a general linear model. To verify the accuracy of the estimating equation, the estimated salt intake of the previous day was calculated using our equation and Tanaka’s equation, which is an estimating equation from spot urine widely used in clinical practice and epidemiological studies to estimate dietary salt intake, and they were compared with the actual salt intake. Results: The results of the analysis showed that model 1 (previous day’s salt intake (g) = 3.62 + 0.64 × urinary Na/K ratio + 0.18 × conductivity (mS/cm) - 0.43 × sex (male 0, female 1)) was the optimal model. Then, salt intake was estimated using model 1 and Tanaka’s equation, and compared with actual salt intake. The Pearson’s product-moment correlation coefficient between the actual and estimated salt intake was r = 0.618 (P < 0.001) and r = 0.573 (P < 0.001) for model 1 estimates and Tanaka’s equation estimates, respectively. The percentages of errors within ±30% from the actual salt intake were 64.2% and 58.4% for model 1 and Tanaka’s equation, respectively. Conclusion: An equation for estimating salt intake was developed using the Na/K ratio and conductivity of overnight urine. Although the applicability of this method to hypertensive patients and the elderly has not been studied and needs to be clarified in the future, the estimating equation developed is simple and may be a useful method for daily monitoring of dietary salt intake. J Clin Med Res. 2021;13(9):479-486 doi: https://doi.org/10.14740/jocmr4596
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How this classification was reachedexpand
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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".