Potassium-Based Sodium Substitutes Impact the Sodium and Potassium Content of Foods
Bibliographic record
Abstract
OBJECTIVE: Potassium-based sodium substitutes (PBSS) can be used to replace sodium during food processing. How potassium and sodium content is associated with PBSS is not known. The objectives of the study were to describe the prevalence of PBSS by sodium content claim category and describe how PBSS are associated with sodium and potassium concentrations by sodium level. DESIGN AND METHODS: This cross-sectional analysis used the July 2018 version of the United States Department of Agriculture's Branded Food Products Database. Products were divided into sodium content claim category and were analyzed for the presence of PBSS. Products with nonmissing values for sodium and potassium were grouped by sodium level and analyzed for the prevalence of PBSS to explore potassium and sodium concentration. Column proportion z-test with the Bonferroni correction was used to explore the occurrence of PBSS by sodium content claim category. Mann-Whitney U-test was used to assess differences in potassium and sodium concentrations across sodium levels and within levels by the presence/absence of PBSS. RESULTS: The prevalence of PBSS in the categories "without a sodium content claim" (2.4%), "lightly salted" (0.5%), and "unsalted" claims (0.6%) were statistically significantly lower than prevalence of PBSS in the "sodium free" (9.5%), "low sodium" (10.3%), and "reduced sodium" claim categories (23.3%; all P < .01). Among the group of products with serving sizes more than 30 g containing PBSS, there was a 357 mg per serving higher median sodium concentration and a 160 mg per serving higher median potassium concentration compared to the group without PBSS (both P < .01). CONCLUSION: In the "reduced sodium" claim category, a higher prevalence of PBSS was found compared to other sodium claim categories. The presence of PBSS was associated with higher potassium and sodium concentrations in foods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".