Estimating 24 Hour Sodium Urine from Spot Urine: A Correlation Model Among Aged 25-64 in Indonesia
Bibliographic record
Abstract
INTRODUCTION: Excess salt intake is known to raise blood pressure and turn increase the risk of cardiovascular disease. Data and information on sodium consumption in Indonesia based is limited, while examination method using 24-hour urine examination is a complex method to apply in population based survey. Objective: to estimate value of spot urinary excretion against 24-hour urine values and correction factors. METHOD: Validation was carried out through 24-hour urine and spot urine examination, in 423 individual aged 25-65 years in t Kebun Kelapa Village, Bogor City, Indonesia in 2017. Urine sodium examination method was carried out by laboratory examination inductively coupled plasma optical emission spectrometry (ICP-OES). Accuracy and precision are valued by paired test correlations and mean differences. The coefficient of determination (R Square) of is calculated for sodium intake estimation and correction factors. RESULTS: The morning value of sodium spot urine has better precision and a stronger correlation with the 24-hour sodium urine than the afternoon spot urine. The difference in average morning sodium urine with 24-hour urine sodium according to age and sex was not significantly different (t test 0.30 and p = 0.77), had a positive and moderate correlation (paired r = 0.50 and p = 0.00). The average difference between the estimated evening urine sodium spot was different (t test = 7.32 and p = 0.00), and the correlation was very weak (r = 0.25; p = 0.00). CONCLUSION: The urine content / sodium spot level was accurate to estimate urine sodium value / content 24 hours.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".