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Record W4283832137 · doi:10.54097/hset.v2i.557

Association Between Dietary Patterns and the Risk of Hypertension Among General Population in China and America

2022· article· en· W4283832137 on OpenAlexaff
Siying Li, Xiaoyan Liang, Tianyu Yao

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsWestern University
Fundersnot available
KeywordsEnvironmental healthMedicineDashChinaIncidence (geometry)PopulationBlood pressureDiseasePublic healthStroke (engine)Internal medicineGeography

Abstract

fetched live from OpenAlex

Hypertension is one of the risk factors of many diseases such as cardiovascular disease and stroke, and it has become increasingly prevalent worldwide. Although elevated blood pressure is related to many different factors, some studies have found that people's dietary patterns seem to be closely related to the development of hypertension. This paper aimed to compare the dietary patterns in China and America and to explore how they affect the incidence of hypertension in both countries. Through analysis, high sodium diets, substandard vegetable intake, and high-temperature cooking methods in both countries were found to be hazard factors that might increase the prevalence of hypertension. The difference was that the high sodium intake in America mainly comes from processed food, while the sodium intake in China mainly comes from salt added during cooking. In addition, the relatively high intake of whole-grain diet in China may also be one of the reasons for the relatively low prevalence of hypertension in China. In terms of intervention on hypertension, although America has higher compliance with the DASH diet, it is still important to popularize dietary guidelines and hypertension-related knowledge in order to help the public better improve their health status. However, the current research has no definite evidence to prove the relationship between diet and hypertension, so more research and data still need to be found.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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 designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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