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Record W4233077522 · doi:10.14740/jocmr4596

Development of a Method for Estimating Dietary Salt Intake Using the Overnight Urinary Sodium/Potassium Ratio

2021· article· en· W4233077522 on OpenAlexvenueno aff
Yuta Sumikama, Hiroshige Aoyama, Norifumi Isu, Masaaki Nagata, Takumi Kato, Takayoshi Tsukahara

Bibliographic record

VenueJournal of Clinical Medicine Research · 2021
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePotassiumSodiumUrineSalt (chemistry)Animal scienceUrinary systemInternal medicineEndocrinologyChemistryBiology

Abstract

fetched live from OpenAlex

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

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.481
GPT teacher head0.608
Teacher spread0.127 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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