Potassium intake: the Cinderella electrolyte
Bibliographic record
Abstract
Dietary guidelines recommend intake targets for some essential minerals, based on observational and experimental evidence relating mineral intake levels to health outcomes. For prevention of cardiovascular disease, reducing sodium intake and increasing potassium intake are the principal tools. While reducing sodium intake has received greatest public health priority, emerging evidence suggests that increasing potassium intake may be a more important target for cardiovascular prevention. Increased potassium intake reduces blood pressure and mitigates the hypertensive effects of excess sodium intake, and the recent large Phase III SSaSS trial reported that increasing potassium intake (and reducing sodium intake) in populations with low potassium intake and high sodium intake, through salt substitution (25% KCl, 75%NaCl), reduces the risk of stroke in patients at increased cardiovascular risk. As key sources of potassium intake include fruit, vegetables, nuts, and legumes, higher potassium intake may be associated with healthy dietary patterns. The current review makes the case that increasing potassium intake might represent a more advantageous dietary strategy for prevention of cardiovascular disease. Future research should focus on addressing the independent effect of potassium supplementation in populations with low or moderate potassium intake, and determine effective strategies to increase potassium intake from diet.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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 teacher head, 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".