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Record W2972623995 · doi:10.3390/nu11092226

An Evaluation of the Sodium Content and Compliance with the National Sodium Reduction Targets among Packaged Foods Sold in Costa Rica in 2015 and 2018

2019· article· en· W2972623995 on OpenAlexafffund
Jaritza Vega-Solano, Adriana Blanco‐Metzler, Karla Benavides-Aguilar, JoAnne Arcand

Bibliographic record

VenueNutrients · 2019
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsOntario Tech University
FundersInternational Development Research Centre
KeywordsSodiumFood scienceHigh sodiumMedicineChemistry

Abstract

fetched live from OpenAlex

High blood pressure is a leading cause of death in Costa Rica, with an estimated mortality rate of 30%. The average household sodium intake is two times higher than the World Health Organization recommendation. The consumption of processed foods is an important and growing contributor to sodium intake. The objective of this study was to describe the sodium content of packaged foods (mg/100 g) sold in Costa Rica in 2015 (n = 1158) and 2018 (n = 1016) and to assess their compliance with the national sodium reduction targets. All 6 categories with national targets were analyzed: condiments, cookies and biscuits, bread products, processed meats, bakery products, and sauces. A significant reduction in mean sodium content was found in only 3 of the 19 subcategories (cakes, tomato-based sauces, and tomato paste). No subcategories had statistically significant increases in mean sodium levels, but seasonings for sides/mains, ham, and sausage categories were at least 15% higher in sodium. Compliance with the national sodium targets among all foods increased from 80% in 2015 to 87% in 2018. The results demonstrate that it is feasible to reduce the sodium content in packaged foods in Costa Rica, but more work is needed to continually support a gradual reduction of sodium in packaged foods, including more stringent sodium targets.

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)0.000
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 score0.004
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.085
GPT teacher head0.336
Teacher spread0.251 · 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

Citations20
Published2019
Admission routes2
Has abstractyes

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