Food Service Workers: Reliable Assessors of Food Intake in Hospitalized Patients
Bibliographic record
Abstract
Early detection of malnutrition in hospitalized patients is of paramount importance. As poor food intake is a marker of malnutrition risk, a simple and accurate method to monitor intake is valuable. This quality assurance project aimed to determine if food service workers (FSW) were able to provide accurate estimates of patient intakes through visually assessing meal trays at an acute care hospital. FSW conducted visual estimates of patient trays after meals using the meal plate pictorial rating scale adapted from the My Meal Intake Tool and translated their estimates into one of 5 consumption levels (0%, 25%, 50%, 75%, or 100%). A total of 401 patient meal estimates were validated using the food weighing method. Spearman's correlations between percent calories consumed (determined by weight) and estimates by FSW were 0.624 (n = 137, P < 0.001), 0.771 (n = 134, P < 0.001), and 0.829 (n = 130, P < 0.001), for breakfast, lunch, and supper, respectively. Paired Wilcoxon tests and the Kruskal-Wallis H test showed that accuracy varied for breakfast, lunch, and supper. The overall sensitivity and specificity of FSW for detecting patient intake ≤50% was 81% and 88%, respectively. These findings identify that FSW can accurately estimate patient intake, contributing an important marker for the detection of malnutrition.
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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 | 0.004 | 0.020 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".