Development of an online tool for sodium intake assessment in Mexico
Bibliographic record
Abstract
Excess sodium intake is associated with adverse health effects, and reducing its intake is a strategy that improves population health. However, estimating sodium intake is challenging and new options for assessment are needed. This review describes the design and development of a web-based, publicly-accessible, dietary sodium intake screening tool (Calculadora de Sodio) for individuals in Mexico. Sodium data from 2017 – 2018 for 3 429 packaged foods, 655 restaurant and cafeteria foods, and 320 home-style meals and street foods (determined by chemical analysis) comprised the 71-question tool. It was piloted with 10 nutrition experts for feedback on content and face validity; and with 30 potential users to test its usability and interface. Improvements were made to content, language, and formatting following the pilot. Its predictive validity will be established in the future. The Calculadora de Sodio provides instant feedback on an individual’s average daily sodium intake, computed by frequency of intake, average number of servings, and sodium content per serving of each sodium-focused food category. This is the first web-based dietary sodium screening tool developed for the general population of Mexico. It is an efficient and practical way to assess sodium intake and can serve as a model for similar tools for other countries and regions.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".