Appetite Loss in Patients with Advanced Cancer Treated at an Acute Palliative Care Unit
Bibliographic record
Abstract
Appetite loss is prevalent in patients with advanced cancer and negatively affects their quality of life. However, understanding of the factors associated with appetite loss is limited. The current study aims to explore characteristics and therapeutic interventions used for patients with and without appetite loss admitted to an acute palliative care unit. Patient characteristics and patient-reported outcome measures (PROMs), using the 11-point numeric rating scale (NRS 0-10), were registered. Descriptive statistics, independent samples T-tests and chi-square tests were utilized for data analysis. Of the 167 patients included in the analysis, 62% (104) had moderate to severe appetite loss at admission, whereof 63% (66) improved their appetite during their hospital stay. At admission, there was a significant association between appetite loss and having gastrointestinal cancer, living alone, poor performance status and withdrawn anticancer treatment. Patients with appetite loss also experienced more nausea, depression, fatigue, dyspnea and anxiety. In patients with improved appetite during hospitalization, mean decrease in NRS was 3.4 (standard error (SE) 0.27). Additionally, patients living alone were more likely to improve their appetite. Appetite improvement frequently coincided with alleviation of fatigue. Understanding these associations may help in developing better interventions for managing appetite loss in patients with advanced cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".