Potassium content of the American food supply and implications for the management of hyperkalemia in dialysis: An analysis of the Branded Product Database
Bibliographic record
Abstract
Ultraprocessed foods can be a source of potassium additives. Excess potassium consumption can lead to hyperkalemia. How frequently potassium additives are found in the food supply and how they impact potassium content is not well documented. Using the Branded Product Database, ingredient lists were searched for "potassium" to identify products containing additives. For products listing potassium content, accuracy of potassium content reporting and how potassium content differed with additive use was also assessed. A total of 239,089 products were included, 35,102 (14.7%) contained potassium additives, and 13,685 (5.7%) provided potassium content. Potassium additives were most commonly found in dairy products, supplements, and mixed foods (at 37%, 34%, and 28%, respectively). Potassium additives in mixed foods and vegetables and fruits were associated with 71% and 28% more potassium per serving, respectively (p < 0.01). Potassium content increased by 1874 mg (66%) when a 1-day sample menu compared foods with and without additives. Potassium content of foods with and without additives is not well documented. Potassium additives are prevalent and can be associated with increased potassium content. However, more information is needed to better understand how different additives used in different foods change potassium content.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".