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Record W4387157755 · doi:10.1093/ajh/hpad094

Potassium and Hypertension: A State-of-the-Art Review

2023· review· en· W4387157755 on OpenAlexafffund
Sriram Sriperumbuduri, Paul A. Welling, Marcel Ruzicka, Gregory L. Hundemer, Swapnil Hiremath

Bibliographic record

VenueAmerican Journal of Hypertension · 2023
Typereview
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersLotte and John Hecht Memorial FoundationUniversity of Ottawa
KeywordsMedicineBlood pressureNatriuresisPotassiumDietary saltStroke (engine)Risk factorPsychological interventionInternal medicineSodiumEndocrinologyCardiologyIntensive care medicineNursing

Abstract

fetched live from OpenAlex

Hypertension is the single most important and modifiable risk factor for cardiovascular morbidity and mortality worldwide. Non pharmacologic interventions, in particular dietary modifications have been established to decrease blood pressure (BP) and hypertension related adverse cardiovascular events. Among those dietary modifications, sodium intake restriction dominates guidelines from professional organizations and has garnered the greatest attention from the mainstream media. Despite guidelines and media exhortations, dietary sodium intake globally has not noticeably changed over recent decades. Meanwhile, increasing dietary potassium intake has remained on the sidelines, despite similar BP-lowering effects. New research reveals a potential mechanism of action, with the elucidation of its effect on natriuresis via the potassium switch effect. Additionally, potassium-substituted salt has been shown to not only reduce BP, but also reduce the risk for stroke and cardiovascular mortality. With these data, we argue that the focus on dietary modification should shift from a sodium-focused to a sodium- and potassium-focused approach with an emphasis on intervention strategies which can easily be implemented into clinical practice.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.341
Teacher spread0.249 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations22
Published2023
Admission routes2
Has abstractyes

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