Formula-led methods using first morning fasting spot urine to assess usual salt intake: a secondary analysis of PURE study data
Bibliographic record
Abstract
OBJECTIVES: Observational studies that assess the relationship between salt intake and long-term outcomes require a valid estimate of usual salt intake. The gold-standard measure in individuals is sodium excretion in multiple nonconsecutive 24-h urines. Multiple studies have demonstrated that random spot urine samples are not valid for estimating usual salt intake; however, some researchers believe that fasting morning spot urine samples produce a better measure of usual salt intake than random spot samples. METHODS: We have used publicly available data from a PURE China validation study to compare estimates of usual salt intake from morning spot urine samples and three published formulae with mean of two 24-h urine samples (reference). We estimated the means and 95% confidence intervals of absolute and relative errors for each formula-led method and the degree to which estimates were able to be classified into the correct quartile of intake. Bland-Altman plots were used to test the level of agreement. RESULTS: The results show that compared with the reference method, all formulae-led estimates from spot urine collections have high error rates: both random and systematic. This is demonstrated for individual estimates, as well as by quartiles of reference salt intake. This study conclusively demonstrates the unsuitability of morning spot urine formula-led estimates of usual salt intake. CONCLUSION: Our findings support international recommendations to not conduct, fund, or publish research studies that use spot urine samples with estimating equations to assess individuals' salt intake in association with health outcomes.
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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 | 0.073 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".