Association between salt substitutes/enhancers and changes in sodium levels in fast-food restaurants: a cross-sectional analysis
Bibliographic record
Abstract
Background: Restaurant foods have high sodium levels, and efforts have been made to promote reductions. The objective of this study was to understand if salt substitutes and enhancers are associated with changes in sodium levels in fast-food restaurants. Methods: A longitudinal database (MENU-FLIP) containing nutrition information for Canadian chain restaurants with 20 or more locations nationally was created in 2010 and updated in 2013 and 2016. In 2016, when available, ingredient lists were collected from restaurant websites and searched for the presence of salt substitutes/enhancers. Changes in sodium levels (per serving) and the prevalence of salt substitutes/enhancers in 222 foods from 12 of the leading fast-food restaurant chains were compared across 3 time points. Results: Sixty-nine percent of foods contained a salt substitute/enhancer. Substitutes/enhancers were found in every restaurant chain (n = 12) for which ingredient data were available. The most common substitutes/enhancers were yeast extracts (in 30% of foods), calcium chloride (28%), monosodium glutamate (14%) and potassium chloride (12%). Sodium levels in foods that contained substitutes/enhancers decreased significantly more (190 ± 42 mg/serving) over the study period than those in foods that did not contain a substitute/enhancer (40 ± 17 mg/serving, p < 0.001). Interpretation: Salt substitutes and enhancers are prevalent in restaurant foods and are one means by which restaurants may be lowering sodium levels in their foods. At this time, the potential consequences of these findings, if any, are uncertain.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".