MétaCan
Menu
Back to cohort
Record W2121499401 · doi:10.3390/nu7115466

A Multi-Center Assessment of Nutrient Levels and Foods Provided by Hospital Patient Menus

2015· article· en· W2121499401 on OpenAlexaffabout
Susan Trang, Jackie Fraser, Lori Wilkinson, Katherine Steckham, Heather Oliphant, Heather Fletcher, Roula Tzianetas, JoAnne Arcand

Bibliographic record

VenueNutrients · 2015
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsOntario Tech UniversitySt. Michael's HospitalUniversity of TorontoHamilton Health SciencesHealth Sciences CentreMount Sinai Hospital
Fundersnot available
KeywordsCalorieDietary Reference IntakeMedicineNutrientMalnutritionVitaminEnvironmental healthFood scienceBiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.061
GPT teacher head0.369
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueNutrientsSame topicNutrition and Health in AgingFrench-language works237,207