Exploring the relationship of self-reported lack of appetite to patient characteristics and symptom burden.
Bibliographic record
Abstract
187 Background: Cancer anorexia-cachexia syndrome (CACS) in patients is associated with decreases in lean body mass and body weight. Self-reported lack of appetite may be an important indicator for early identification of CACS. The current analyses examined the relationship of perceived lack of appetite to patient characteristics and overall symptom burden in a large mixed cancer sample referred to a palliative care clinic. Methods: We conducted a retrospective review of patients newly referred to an outpatient palliative care clinic over a two-year period. Data on demographic and clinical characteristics and patient-reported symptom scores on the Edmonton Symptom Assessment Scale (ESAS) were abstracted. Pearson’s correlations and ANOVAs were used to assess relationships between variables. Multiple regression analysis was used to evaluate the relative contribution of variables that were significantly correlated with lack of appetite at the univariate level. Results: Data on 544 patients ( M=53.7 years) showed that older age (r=12, p<.01), not being married or in a marriage-like relationship (r=.09, p=.04), having insurance other than managed care insurance (r=.10, p=.02), lower body mass index (BMI; r=.11, p<.01), marijuana use (r=.18, p<.0001), and overall symptom burden (ESAS total score r=.52, p < .0001) were associated with worse lack of appetite ( M=3.5, SD=3.1). Patients who were underweight (BMI <18.5, 46.7%) reported significantly worse lack of appetite than patients who were normal weight, overweight, or obese ( M=3.9, SD=3.2, p<.01). The final hierarchical regression model accounted for 34% of the variance in lack of appetite, with age, marital status, BMI, marijuana use, and total symptom burden remaining significant independent correlates (p’ s <.01). Conclusions: Contrary to expectations, relatively few clinical correlates were associated with self-reported lack of appetite. Future research should explore inter-individual genetic factors to explain alterations in lean body mass and body weight that may contribute to poor appetite in patients. Such factors may be important indicators for early identification of CACS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".