Symptoms, performance status and quality of life in cancer patients receiving palliative care
Bibliographic record
Abstract
Aim: The aim of the study was to describe the symptoms experienced by cancer patients receiving palliative care, patients’ performance and the effects on their quality of life. Materials and Methods: This is a descriptive study and was conducted with 106 patients admitted to palliative care unit at a university hospital in Izmir, located in the west of Turkey, between December 2019 and April 2020. For data collection, Patient Information Form, “Eastern Cooperative Oncology Group (ECOG) Performance Status Scale”, “Edmonton Symptom Assessment Scale (ESAS)” and “Functional Assessment of Chronic Illness Therapy-Palliative Care (FACIT-Pal) Scale” were applied. For data analysis, descriptive statistics, Chi-square test, Kruskall Wallis Analysis and linear regression analysis were used. Results: Patients reported that the most common symptoms experienced were fatigue, sense of being unwell, anxiety, sadness (depression) and pain. According to the regression analysis, there was a statistically significant difference between the total quality of life scores of the patients and pain, fatigue and nausea from the patients' ESAS symptoms. The quality of life scores were significantly lower in the patients who were hospitalized, had an advanced disease stage, did not have metastases or did not know their metastases status and had a low performance status ECOG. There was a statistically significant difference between patients' ECOG performance status and quality of life. Conclusion: Patients have multiple symptoms and poor quality of life. Our findings support the importance of symptom assessment and management to improve quality of life.
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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 | 0.004 |
| 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".