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Record W2594122804 · doi:10.1111/jch.12970

The science of salt: A regularly updated systematic review of salt and health outcomes (December 2015–March 2016)

2017· review· en· W2594122804 on OpenAlexaff
Michelle Wong, JoAnne Arcand, Alexander A. C. Leung, Sudhir Raj Thout, Norm R.C. Campbell, Jacqui Webster

Bibliographic record

VenueJournal of Clinical Hypertension · 2017
Typereview
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of CalgaryOntario Tech UniversityUniversity of British Columbia
FundersNational Health and Medical Research CouncilVicHealthWorld Health Organization
KeywordsMedicineObesityNonalcoholic fatty liver diseaseEnvironmental healthIncidence (geometry)Blood pressureDiseaseMEDLINEDietary saltCritical appraisalIntensive care medicineInternal medicineGerontologyAlternative medicineFatty liverPathology

Abstract

fetched live from OpenAlex

The purpose of this review was to identify, summarize, and critically appraise studies on dietary salt relating to health outcomes that were published from December 2015 to March 2016. The search strategy was adapted from a previous systematic review on dietary salt and health. Overall, 13 studies were included in the review: one study assessed cardiovascular events, nine studies assessed prevalence or incidence of blood pressure or hypertension, one study assessed kidney disease, and two studies assessed other health outcomes (obesity and nonalcoholic fatty liver disease). Four studies were selected for detailed appraisal and commentary. One study met the minimum methodologic criteria and found an increased risk associated with lower sodium intake in patients with heart failure. All other studies identified in this review demonstrated positive associations between dietary salt and adverse health outcomes.

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)0.011
metaresearch head score (Gemma)0.057
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 score0.016
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.057
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0160.013
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.335
GPT teacher head0.541
Teacher spread0.207 · 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

Citations77
Published2017
Admission routes1
Has abstractyes

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