Prevalence and nutrient composition of menu offerings targeted to customers with dietary restrictions at US fast casual and full-service restaurants
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence and nutrient composition of menu offerings targeted to customers with dietary restrictions at US fast casual and full-service chain restaurants. DESIGN: We used 2018 data from MenuStat, a database of nutrient information for menu items at large US chain restaurants. Five alternative diets were examined: gluten-free, low-calorie, low-carbohydrate, low-fat and vegetarian. Diet offerings were identified by searching MenuStat item descriptions and reviewing online menus. For each diet, we reported counts and proportions. We used bootstrapped multilevel models to examine differences in predicted mean kilojoules, saturated fat, Na and sugars between diet and non-diet menu items. SETTING: Forty-five US fast casual and full-service chain restaurants in 2018 (including 6419 items in initial analytic sample across small plates, salads and main dishes). PARTICIPANTS: None. RESULTS: The most prevalent diets were gluten-free (n 631, 9·8 % of menu items), low-calorie (n 306, 4·8 %) and vegetarian (n 230, 3·6 %). Compared with non-diet counterparts, low-calorie main dishes had significantly lower levels of all nutrients examined and vegetarian main dishes had significantly lower levels of all nutrients except saturated fat. Gluten-free small plates had significantly fewer kilojoules, grams of saturated fat and milligrams of Na compared with non-diet small plates. CONCLUSIONS: A small proportion of fast casual and full-service restaurant menus are targeted towards customers with dietary restrictions. Compared with non-diet items, those classified as gluten-free, low-calorie or vegetarian generally have healthier nutrient profiles, but overall nutrient values are still too high for most menu items, regardless of dietary label.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".