The Science of Salt: A global review on changes in sodium levels in foods
Bibliographic record
Abstract
This review aims to summarize and synthesize studies reporting on changes in sodium levels in packaged food products, restaurant foods, and hospital or school meals, as a result of salt reduction interventions. Studies were extracted from those published in the Science of Salt Weekly between June 2013 and February 2018. Twenty-four studies were identified: 17 assessed the changes in packaged foods, four in restaurant foods, two in hospital or school meals, and one in both packaged and restaurant foods. Three types of interventions were evaluated as part of the studies: voluntary reductions (including targets), labeling, and interventions in institutional settings. Decreases in sodium were observed in all studies (n = 8) that included the same packaged foods matched at two time points, and in the studies carried out in hospitals and schools. However, there was little to no change in mean sodium levels in restaurant foods. The pooled analysis of change in sodium levels in packaged foods showed a decrease in sodium in unmatched food products (-36 mg/100 g, 95% CI -51 to -20 mg/100 g) and in five food categories-breakfast cereals, breads, processed meats, crisps and snacks, and soups. Twenty-two of the 24 studies were from high-income countries, limiting the applicability of the findings to lower resource settings.
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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".