Factors associated with palliative care symptoms in cancer patients in Palestine
Bibliographic record
Abstract
Palliative care is critical to redundancy in cancer patients seeking to improve their quality of life. Evaluation should be incorporated into clinical practice routines at all stages of cancer. The Edmonton Symptom Assessment System (ESAS) was used to rate the intensity of ten symptom evaluations designed and validated for cancer patients in various languages and cultures. Therefore, the study aims to assess the symptoms reported using ESAS scores to identify patients who would benefit from palliative care that can improve the integration of palliative care into standard cancer care at An-Najah National University Hospital (NNUH). A cross-sectional study was selected for 271 cancer patients using a convenience sampling method at NNUH. Demographic, clinical, and lifestyle characteristics are described. Furthermore, patients' moderate to severe symptoms (score > 4) were obtained using ESAS-R. The survey consisted of 271 patients, with a response rate of 95%. The average age of the patients was 47 ± 17.7 years, ranging from 18 to 84 years. The male-to-female ratio was approximately 1:1, 59.4% of the patients were outpatients, and 153 (56.5%) had hematologic malignancies. Fatigue (62.7%) and drowsiness (61.6%) were the most common moderate to severe symptoms in ESAS. Furthermore, pain (54.6%), nausea (40.2%), lack of appetite (55.0%), shortness of breath (28.5%), depression (40.6%), anxiety (47.2%) and poor well-being (56.5%) were reported. In conclusion, fatigue and drowsiness were the most reported symptoms according to the ESAS scale among cancer patients, while moderate to severe symptoms were reported in cancer patients using the ESAS. The ESAS is a functional tool for assessing cancer patients' symptoms and establishing palliative care services.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".