A Multi-Center Assessment of Nutrient Levels and Foods Provided by Hospital Patient Menus
Bibliographic record
Abstract
Diets of high nutritional quality can aid in the prevention and management of malnutrition in hospitalized patients. This study evaluated the nutritional quality of hospital patient menus. At three large acute care hospitals in Ontario, Canada, 84 standard menus were evaluated, which included regular and carbohydrate-controlled diets and 3000 mg and 2000 mg sodium diets. Mean levels of calories, macronutrients and vitamins and minerals provided were calculated. Comparisons were made with the Dietary Reference Intakes (DRI) and Canada's Food Guide (CFG) recommendations. Calorie levels ranged from 1281 to 3007 kcal, with 45% of menus below 1600 kcal. Protein ranged from 49 to 159 g (0.9-1.1 g/kg/day). Energy and protein levels were highest in carbohydrate-controlled menus. All regular and carbohydrate-controlled menus provided macronutrients within the Acceptable Macronutrient Distribution Ranges. The proportion of regular diet menus meeting the DRIs: 0% for fiber; 7% for calcium; 57% for vitamin C; and 100% for iron. Compared to CFG recommended servings, 35% met vegetables and fruit and milk and alternatives, 11% met grain products and 8% met meat and alternatives. These data support the need for frequent monitoring and evaluation of menus, food procurement and menu planning policies and for sufficient resources to ensure menu quality.
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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 | 0.001 | 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".