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Record W2076190877 · doi:10.1097/hjh.0000000000000079

The International Society of Hypertension and World Hypertension League call on governments, nongovernmental organizations and the food industry to work to reduce dietary sodium

2014· article· en· W2076190877 on OpenAlexaff
Norman R.C. Campbell, Daniel T. Lackland, Arun Chockalingam, Stephen Harrap, Rhian M. Touyz, Louise M. Burrell, Agustín J. Ramiréz, Roland E. Schmieder, Aletta E. Schutte, Dorairaj Prabhakaran, Ernesto L. Schiffrin

Bibliographic record

VenueJournal of Hypertension · 2014
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsLibin Cardiovascular Institute of AlbertaJewish General HospitalUniversity of TorontoUniversity of Alberta HospitalMcGill UniversityUniversity of Calgary
FundersPan American Health Organization
KeywordsMedicineDietary SodiumLeagueDietary saltWork (physics)Mission statementStatement (logic)Economic growthPublic relationsBlood pressurePolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

The International Society of Hypertension and the World Hypertension League have developed a policy statement calling for reducing dietary salt. The policy supports the WHO and the United Nations recommendations, which are based on a comprehensive and up-to-date review of relevant research. The policy statement calls for broad societal action to reduce dietary salt, thus reducing blood pressure and preventing hypertension and its related burden of cardiovascular disease. The hypertension organizations and experts need to become more engaged in the efforts to prevent hypertension and to advocate strongly to have dietary salt reduction policies implemented. The statement is being circulated to national hypertension organizations and to international nongovernmental health organizations for consideration of endorsement. Member organizations of the International Society of Hypertension and the World Hypertension League are urged to support this effort.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.252
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations18
Published2014
Admission routes1
Has abstractyes

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