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Record W1997991173 · doi:10.1111/jch.12479

Using the Global Burden of Disease Study to Assist Development of Nation‐Specific Fact Sheets to Promote Prevention and Control of Hypertension and Reduction in Dietary Salt: A Resource From the World Hypertension League

2015· article· en· W1997991173 on OpenAlexaff
Norm R.C. Campbell, Daniel T. Lackland, Mark L. Niebylski, Peter M. Nilsson, Xinhua Zhang

Bibliographic record

VenueJournal of Clinical Hypertension · 2015
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicinePsychological interventionEnvironmental healthDiseaseBurden of diseaseGlobal healthPublic healthHealth careIntensive care medicineEconomic growthNursingPopulationPathology

Abstract

fetched live from OpenAlex

Increased blood pressure and high dietary salt are leading risks for death and disability globally. Reducing the burden of both health risks are United Nations' targets for reducing noncommunicable disease. Nongovernmental organizations and individuals can assist by ensuring widespread dissemination of the best available facts and recommended interventions for both health risks. Simple but impactful fact sheets can be useful for informing the public, healthcare professionals, and policy makers. The World Hypertension League has developed fact sheets on dietary salt and hypertension but in many circumstances the greatest impact would be obtained from national-level fact sheets. This manuscript provides instructions and a template for developing fact sheets based on the Global Burden of Disease study and national survey data.

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)0.019
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.074
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0340.014

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 head0.259
GPT teacher head0.409
Teacher spread0.150 · 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 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

Citations64
Published2015
Admission routes1
Has abstractyes

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