Monitoring sodium content in processed and ultraprocessed foods in Argentina 2022: compliance with National Legislation and Regional Targets
Bibliographic record
Abstract
Abstract Objective: To assess the current Na levels in a variety of processed food groups and categories available in the Argentinean market to monitor compliance with the National Law and to compare the current Na content levels with the updated Pan American Health Organisation (PAHO) regional targets. Design: Observational cross-sectional study. Setting and Participants: Argentina. Data were collected during March 2022 in the city of Buenos Aires in two of the main supermarket chains. We carried out a systematic survey of pre-packaged food products available in the food supply assessing Na content as reported in nutrition information panels. Results: We surveyed 3997 food products, and the Na content of 760 and 2511 of them was compared with the maximum levels according to the Argentinean law and the regional targets, respectively. All food categories presented high variability of Na content. More than 90 % of the products included in the National Sodium Reduction Law were found to be compliant. Food groups with high median Na, such as meat and fish condiments, leavening flour and appetisers are not included in the National Law. In turn, comparisons with PAHO regional targets indicated that more than 50 % of the products were found to exceed the regional targets for Na. Conclusions: This evidence suggests that it is imperative to update the National Sodium Reduction Law based on regional public health standards, adding new food groups and setting more stringent legal targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".