Serum Sodium and Potassium Distribution and Characteristics in the US Population, National Health and Nutrition Examination Survey 2009–2016
Bibliographic record
Abstract
BACKGROUND: Concern has been expressed by some that sodium reduction could lead to increased prevalence of hyponatremia and hyperkalemia for specific population subgroups. Current concentrations of serum sodium and potassium in the US population can help address this concern. METHODS: We used data from the National Health and Nutrition Examination Survey 2009-2016 to examine mean and selected percentiles of serum sodium and potassium by sex and age group among 25 520 US participants aged 12 years or older. Logistic regression models with predicted residuals were used to examine the age-adjusted prevalence of low serum sodium and high serum potassium among adults aged 20 or older by selected sociodemographic characteristics and by health conditions or medication use. RESULTS: The distributions of serum sodium and potassium concentrations were within normal reference intervals overall and across Dietary Reference Intake life-stage groups, with a few exceptions. Overall, 2% of US adults had low serum sodium (<135 mmol/L) and 0.6% had high serum potassium (>5 mmol/L). Prevalence of low serum sodium and high serum potassium was higher among adults aged 71 or older (4.7 and 2.0%, respectively) and among adults with chronic kidney disease (3.4 and 1.9%), diabetes (5.0 and 1.1%), or using certain medications (which varied by condition), adjusted for age; whereas, prevalence was <1% among adults without these conditions or medications. CONCLUSIONS: Most of the US population has normal serum sodium and potassium concentrations; these data describe population subgroups at higher risk of low serum sodium and high serum potassium and can inform clinical care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".