Impact of fractional excretion of sodium on a single morning void urine collection as an estimate of 24‐hour urine sodium
Bibliographic record
Abstract
The standard for assessing dietary sodium intake is to measure 24-hour urine sodium. On average, 93% of daily sodium intake is excreted over 24-hours. Expense and difficulties in obtaining complete 24-hour collections have led to the measurement of sodium concentration in spot and single-void urine samples, using predictive equations to estimate 24-hour urine sodium. Although multiple predictive equations have been developed, in addition to having an average bias, all the equations overestimate 24-hour sodium at lower levels of 24-hour sodium and underestimate 24-hour urine sodium at higher levels of 24-hour sodium. One of the least biased estimating equations is the INTERSALT equation, which incorporates a spot urine creatinine concentration. The authors hypothesized that differential fractional excretion of sodium (FeNa)(derived from a morning void collection) relative to creatinine would impact on the accuracy of the INTERSALT equation in estimating 24-hour urine sodium. In a prospective study of 139 adults aged 65 years and over, three sequential morning void and 24-hour urine samples were examined. There was a significant correlation between increasing FENa and the difference between estimated and measured 24-hours urine sodium (r = 0.358, P < .01). In the lowest quartile of FENa, the INTERSALT equation overestimated 24-hour urine sodium, but underestimated 24-hour urine sodium with greater magnitude in each of the subsequent quartiles of FENa. Differential excretion of sodium relative to creatinine, potentially impacted by renal blood flow and hydration, among other factors, affected the accuracy of the INTERSALT equation. Additional research may refine the INTERSALT and other predictive equations to increase their accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".