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Record W2118215550 · doi:10.1093/nutrit/nuu003

Possible deleterious hormonal changes associated with low-sodium diets

2014· review· en· W2118215550 on OpenAlexaffabout
Jaime L. Clark, Leandro Gazziero Rech, Nazia Chaity, Jyoti Sihag, Carla G. Taylor, Michel Aliani

Bibliographic record

VenueNutrition Reviews · 2014
Typereview
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsUniversity of ManitobaHealth Sciences Centre
Fundersnot available
KeywordsHormoneSodiumEndocrinologyAldosteroneInternal medicineGlycemicMedicineRenin–angiotensin systemPhysiologyDiabetes mellitusBlood pressureChemistry

Abstract

fetched live from OpenAlex

The average salt intake of people in Canada, the United States, and Europe is about 3,400 mg of sodium per day, which exceeds the recommended intake levels set by various health organizations. The World Health Organization recommends a worldwide reduction of sodium intake to less than 2,000 mg per day. Most research to date has focused on the negative effects of high-sodium intake; however, little information is available on the metabolic effects of low-sodium intakes. This review focuses on the hormonal changes associated with low-sodium diets, especially the hormones involved in metabolism and cardiovascular and renal function. Based largely on rodent studies, low-sodium diets have been associated with changes in glycemic control, energy metabolism, cardiovascular disease risk, cholesterol concentrations, inflammation, and functioning of the renin-angiotensin-aldosterone system. Overall, research has revealed mixed results regarding the impact of dietary sodium intake on various hormones. Further research is required to assess the effects of sodium reduction on hormones and their associated pathways in order to determine the likelihood of any unintended effects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.072
GPT teacher head0.356
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations4
Published2014
Admission routes2
Has abstractyes

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