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Record W4378976378 · doi:10.1152/ajprenal.00007.2023

Less sodium, more potassium, or both: population-wide strategies to prevent hypertension

2023· review· en· W4378976378 on OpenAlexaff
Voravech Nissaisorakarn, George Ormseth, William James Earle, Martha Catalina Morales-Álvarez, Swapnil Hiremath, Stephen P. Juraschek

Bibliographic record

VenueAmerican Journal of Physiology-Renal Physiology · 2023
Typereview
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute on Minority Health and Health DisparitiesNational Heart, Lung, and Blood Institute
KeywordsContext (archaeology)MedicineKidney diseaseStroke (engine)PopulationIntensive care medicineDiseaseSodiumPotassiumEnvironmental healthInternal medicineGeography

Abstract

fetched live from OpenAlex

Hypertension is among the most prevalent medical conditions globally and a major contributor to chronic kidney disease, cardiovascular disease, and death. Prevention through nonpharmacological, population-level interventions is critically needed to halt this worldwide epidemic. However, there are ongoing disagreements as to where public policy efforts should focus. Recently the Salt Substitute and Stroke Study demonstrated the efficacy of substituting table salt with potassium salt to reduce the risk of stroke, major cardiovascular events, and death. However, this sparked debate over whether sodium or potassium should be prioritized in countries where table salt substitution was less feasible. In this commentary, we summarize arguments in favor of either strategy: reduced sodium or increased potassium intake. Moreover, we discuss evidence and policy approaches related to either or combined approaches relevant to cultural context. Ultimately, there is an urgent need for policies that both reduce sodium and increase potassium intake; however, identifying a strategy that fits cultural context will be key to improve population-wide blood pressures.

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)0.000
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 score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.078
GPT teacher head0.378
Teacher spread0.299 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Physiology-Renal PhysiologySame topicSodium Intake and HealthFrench-language works237,207