Low food intake in hospital: patient, institutional, and clinical factors
Bibliographic record
Abstract
In-hospital malnutrition and inadequate food intake have been associated with negative outcomes (e.g., prolonged length of stay, readmission, mortality, and increased hospital costs). Studies examining the factors associated with low food intake in hospital, commonly defined as the consumption of ≤50% of meals, have produced mixed results. We examined the correlates of food intake including patient socioeconomic, demographic, and health characteristics, institutional factors, and common clinical strategies in 1129 medical patients from 5 Canadian hospitals. Low food intake was found in 35% of patients (41% of females and 29% of males) (p < 0.001). In multivariate analyses, sex, socioeconomic status, demographics, and diagnoses were not significantly related to food intake. Patients assessed as malnourished (subjective global assessment (SGA) B/C) (odds ratio (OR), 2.41; p = 0.003) or as not at risk of malnutrition (OR, 1.67; p = 0.040) were more likely to have low intake when compared with those assessed as well nourished (SGA A). Patient reports of mealtime challenges (OR, 2.70; p < 0.001) and barriers to food intake (OR, 1.11; p = 0.008) were positively related to low intake throughout the study sample. Higher 12-Item Short Form Health Survey Mental Component Summary scores were related to better food intake (OR, 0.98; p < 0.001). Clinical strategies such as between-meal snacks lowered the likelihood of low food intake (OR, 0.55; p = 0.037), whereas a group of "other strategies" increased the odds (OR, 2.77; p = 0.001). These results offer a better understanding of the correlates of in-hospital low food intake. The conclusion discusses some avenues for improving food intake in the clinical setting, such as better mealtime monitoring and a reduction in barriers to food intake.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".