Symptoms in the Lives of Terminal Cancer Patients: Which Is the Most Important?
Bibliographic record
Abstract
OBJECTIVES: Symptoms other than their primary disease can interfere in the lives of terminal cancer patients. We sought to identify which of these symptoms is most important. METHODS: We administered a questionnaire, including the M.D. Anderson Symptom Inventory (MDASI), to 142 terminal cancer patients at the National Cancer Center, Korea. The validity of the MDASI was tested by principal-axis factor analysis and Cronbach's alpha coefficient. Stepwise multiple regression analysis was used to determine the symptoms that interfered most in terminal cancer patients' lives. RESULTS: Factor analysis showed that it was composed of two factors (symptom and interference scales). Cronbach's alpha coefficients of symptom and interference scales were each >0.70. The patients had an average of 11 of 13 symptoms of the MDASI. Pain was the most common and severe, followed by feelings of distress and fatigue. Fatigue was the most highly correlated with interference sum. In stepwise multiple regression analysis, the most interfering symptom was fatigue. CONCLUSIONS: Although pain was the most common and severe symptom, fatigue was the most important symptom interfering in the lives of terminal cancer patients. In treating terminal cancer patients, healthcare providers should actively intervene to reduce both fatigue and pain.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".