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Record W2729564211 · doi:10.1111/jch.13044

The impact of voluntary targets on the sodium content of processed foods in Brazil, 2011–2013

2017· article· en· W2729564211 on OpenAlexaff
Eduardo Augusto Fernandes Nilson, Ana Maria Spaniol, Vivian Siqueira Santos Gonçalves, Michele Lessa de Oliveira, Norm R.C. Campbell, Mary R. L’Abbé, Patrícia Constante Jaime

Bibliographic record

VenueJournal of Clinical Hypertension · 2017
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsMedicineFood scienceFood labelingEnvironmental health

Abstract

fetched live from OpenAlex

Brazilians consume excessive dietary sodium (4700 mg/d); hence, the reduction of dietary sodium intake has been a Brazilian government priority. A set of strategies has been implemented that includes food and nutrition education initiatives and the reduction in the sodium content of processed foods and foods consumed out of the households. Since 2011, the Ministry of Health has selected priority food categories that contribute to over 90% of sodium intake from processed foods and have set biannual voluntary targets for sodium reduction with food industries to encourage food reformulation. Three rounds of monitoring of the sodium content on food labels have been conducted for instant pasta, commercially produced breads, cakes and cake mixes, cookies and crackers, snacks, chips, mayonnaise, salt-based condiments, and margarine. Between 90% and 100% of the food products achieved the first targets in the 2011-2013 period, and the average sodium content of food categories was reduced from 5% to 21% in these first 2 years. These data show that with close monitoring and government oversight, voluntary targets to reduce the sodium content in processed foods can have a significant impact even in a short time frame. The Brazilian strategy will be continuously monitored to maximize its impact, and, if necessary in the future, a transition to regulatory approaches with stronger enforcement may be considered.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.266
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.212
GPT teacher head0.449
Teacher spread0.237 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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