Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Malnutrition has a direct impact on both the toxicities of cancer therapy and the overall survival of oncological patients. However, its prevalence amongst vulnerable groups such as older patients (age ≥ 65 years) is often underestimated. Screening tools recognizing patients at risk are well established, yet they do not take into account that cancer therapy may lead to changes in dietary habits or that therapy’s side effects may negatively influence nutritional status. Methods: To close this gap, we combined the validated Nutritional Risk Score 2002 (NRS-2002) and G8 screening tools with short questionnaires addressing diet changes and symptom load and screened 300 cancer inpatients between 12/2022 and 12/2023. Descriptive statistics (Fisher’s exact, Student’s t-test) as well as heat mapping were applied for data analysis. Results: Overall, two in three inpatients ≥65 years were at risk for malnutrition, and the majority of patients (87.67%) scored ≤14 points on the G8 and were considered frail. Surprisingly, the symptom complex of oral discomfort was most often mentioned by patients (xerostomia—178/300 patients, loss of appetite: 122/300 patients, dysgeusia: 93/300 patients). Diet changes were also common, with patients mainly avoiding certain foods (122/300 patients) or using dietary supplements (106/300 patients). Conclusions: Taken together, older cancer inpatients are frail and have a high risk of malnutrition. Screening should not only consider energy intake but also symptom burden and dietary changes to optimize supportive care.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".