Poor Appetite and Survival in Patients Admitted to an Acute Palliative Care Unit for Comprehensive Palliative Care
Bibliographic record
Abstract
Background/Objectives: Loss of appetite is a common symptom in patients with advanced cancer, and may contribute to patient deterioration. There is a lack of information about this issue, particularly in patients with advanced cancer admitted to an acute palliative care unit. The aims of this study were to assess appetite loss in patients admitted to an APCU and to investigate whether changes following comprehensive palliative care treatment are associated with survival. Materials and Methods: A consecutive sample of 520 patients admitted to the APCU was assessed. Patient characteristics and Edmonton Symptom Assessment Scale (ESAS) were measured at admission (T0) and after one week of comprehensive palliative care treatment (T7). Results: Of 381 patients screened, 208 (54.6%) had a poor appetite rating (≥4/10). Following comprehensive palliative care (T7), the number of patients with poor appetite significantly decreased to 116 (30%) (p < 0.0005). A multivariate regression analysis revealed that nausea (p = 0.002), weakness (p = 0.006), poor well-being (p = 0.017), and total ESAS score were correlated with poor appetite at T0. At T7, pain (p = 0.018), anxiety (p = 0.001), depression (p = 0.014), poor sleep (p = 0.047), drowsiness (p = 0.035), nausea (p = 0.018), weakness (p < 0.0005), poor well-being (p < 0.0005), and total ESAS score (p < 0.0005) were correlated with poor appetite. Survival was associated with a low Karnofsky (OR = 3.217(1.310–5.124), p = 0.001) and the presence of poor appetite at T7 (OR = −7.772(−14.662–−882), p = 0.027). Conclusions: A large proportion of patients admitted to an APCU present moderate-to-severe poor appetite. Clinical improvement of poor appetite is associated with improved survival.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".