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Record W3120573899 · doi:10.1371/journal.pone.0245388

Impact of salt intake reduction on CVD mortality in Costa Rica: A scenario modelling study

2021· article· en· W3120573899 on OpenAlexafffund
Jaritza Vega-Solano, Adriana Blanco‐Metzler, Karol Madriz-Morales, Eduardo Augusto Fernandes Nilson, Marie‐Ève Labonté

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsUniversité Laval
FundersInternational Development Research Centre
KeywordsEnvironmental healthMedicineStroke (engine)Mortality rateBurden of diseasePopulationDemographyConsumption (sociology)DiseaseCause of deathBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

Cardiovascular diseases (CVD) represent the leading cause of death in Costa Rica and high blood pressure was associated with a mortality rate of 29% in 2018. The average household sodium intake in the country is also two times higher than the World Health Organization recommendation. The objective of this study was to estimate the impact of reducing salt intake on CVD mortality in Costa Rica using a scenario simulation model. The Preventable Risk Integrated ModEl (PRIME) was used to estimate the number of deaths that would be averted or delayed in the Costa Rican population by following the national and the international guidelines to reduce salt consumption, according to two scenarios: A) 46% reduction and B) 15% reduction, both at an energy intake of 2171 kcal. The scenarios estimated that between 4% and 13%, respectively, of deaths due to CVD would be prevented or postponed. The highest percentages of deaths prevented or postponed by type of CVD would be related to Coronary heart disease (39% and 38%, respectively), Hypertensive disease (32% and 33%, respectively), and Stroke (22% in both). The results demonstrate that reducing salt consumption could prevent or postpone an important number of deaths in Costa Rica. More support for existing policies and programs urges.

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 score0.033
Threshold uncertainty score0.764

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 head0.160
GPT teacher head0.353
Teacher spread0.193 · 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

Citations25
Published2021
Admission routes2
Has abstractyes

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