Hunger and Aversion: Drives That Influence Food Intake of Hospitalized Geriatric Patients
Bibliographic record
Abstract
BACKGROUND: Diminished appetite occurs frequently with aging and is considered an important clinical symptom of malnutrition, a condition associated with negative clinical outcome, decreased quality of life, and increased health care costs in hospitalized geriatric patients. Yet, in this population, research is scant on hunger and aversion, the two underlying drives that shape appetite, or on their influence on food intake. This study aimed (a) to examine their interrelationship and respective contribution to food intake; (b) to determine how each relate to other health-related contemporaneous subjective states preceding the meal (good physical health, positive mood, pain); and (c) to explore clinical variables as moderators of the drives-intake relationships to identify population segments for which these relationships are the strongest. METHODS: 32 patients (21 women, 11 men; age range, 65-92 years) were observed during repeated meals in a geriatric rehabilitation unit (for a total of 1477 meals). Perceived hunger, aversion, and contemporaneous subjective states were reported before each meal. Protein and energy consumption was calculated from plate leftovers. Clinical measures were obtained from participants' medical charts. RESULTS: The hunger-aversion relationship had a low inverse correlation (p =.001), with each uniquely contributing to protein intake (positive and negative effects, respectively; all p <.05). Hunger was positively associated with the perception of physical health and with mood (all p =.001). Aversion was associated with pain (p =.001). Furthermore, aversion-intake relationships were influenced by moderators, whereas hunger-intake relationships remained constant. CONCLUSIONS: From a clinical perspective, these results suggest that nutritional interventions aimed at bolstering hunger and curbing aversion may be necessary to ensure optimal food intake. Subgroups of patients who would particularly benefit from these interventions are suggested.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".