Prevalence, Intensity, and Prognostic Significance of Common Symptoms in Terminally Ill Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Quality of life and palliative management of end-stage cancer patients should improve with greater understanding of the prevalence, intensity, and prognostic significance of their symptoms. OBJECTIVE: We investigated the association between prevalence and intensity of common symptoms and overall survival in Chinese end-stage cancer patients. DESIGN: For this cross-sectional study, 163 Chinese patients with end-stage cancer completed an Edmonton Symptom Assessment questionnaire, and each was given a Karnofsky Performance Status (KPS) score. Overall survival was estimated via the Kaplan-Meier method. Factors affecting overall survival were determined by univariate and multivariate Cox regression analyses. RESULTS: Mean survival of these patients was 51 days. Pain, lack of appetite, and poor well-being were the most frequent symptoms, in 90.2%, 88.3%, and 87.7%, respectively. The most severe symptoms were fatigue, lack of appetite, drowsiness, and poor well-being. Fatigue, lack of appetite, drowsiness, shortness of breath, poor well-being, depression, and KPS score significantly affected overall survival rate, with a relative risk of dying of 1.560, 2.320, 1.684, 1.295, 1.912, 1.414, and 0.487, respectively (Cox regression coefficients: 0.361, 0.827, 0.539, 0.185, 0.694, 0.318, and -0.602). Fatigue, lack of appetite, shortness of breath, age, and KPS score were independent risk factors of overall survival, with a relative risk of dying of 1.581, 1.122, 1.123, 1.022, and 0.797, respectively (Cox regression coefficients: 0.458, 0.115, 0.116, 0.022, and -0.227). CONCLUSION: Fatigue, shortness of breath, lack of appetite, age, and KPS score were associated with overall survival of end-stage Chinese cancer patients.
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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 | 0.000 | 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".