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Record W2750286375 · doi:10.1111/jch.13049

More evidence that salt increases blood pressure and risk of kidney disease from the Science of Salt: A regularly updated systematic review of salt and health outcomes (April–July 2016)

2017· review· en· W2750286375 on OpenAlexafffund
JoAnne Arcand, Michelle Wong, Joseph Alvin Santos, Alexander A. C. Leung, Kathy Trieu, Sudhir Raj Thout, Jacqui Webster, Norm R.C. Campbell

Bibliographic record

VenueJournal of Clinical Hypertension · 2017
Typereview
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of British ColumbiaUniversity of CalgaryOntario Tech University
FundersPan American Health OrganizationUniversity of Toronto
KeywordsMedicineDietary saltDiseaseBlood pressureIntensive care medicineKidney diseaseMEDLINESystematic reviewEnvironmental healthGerontologyInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this review is to identify, summarize, and critically appraise studies on dietary salt and health outcomes that were published from April to July 2016. The search strategy was adapted from a previous systematic review on dietary salt and health. We have revised our criteria for methodological quality and health outcomes, which are applied to select studies for detailed critical appraisals and written commentary. Overall, 28 studies were identified and are summarized in this review. Four of the 28 studies met criteria for methodological quality and health outcomes and five studies underwent detailed critical appraisals and commentary. Three of these studies found adverse effects of salt on health outcomes (chronic kidney disease and blood pressure) and two were neutral (fracture risk/bone mineral density and cognitive impairment).

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)0.022
metaresearch head score (Gemma)0.101
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.101
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.010
Bibliometrics0.0140.011
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.242
GPT teacher head0.468
Teacher spread0.226 · 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

Citations29
Published2017
Admission routes2
Has abstractyes

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