Fatigue Prevalence Among Palliative Care Cancer Patients in Comprehensive Cancer Center, King Fahad Medical City, Riyadh, Saudi Arabia: A Cross-Sectional Study
Bibliographic record
Abstract
Objective: The aims of the current study are to assess the prevalence of cancer-related fatigue, to examine the difference in cancer-related fatigue severity in relation to patients’ characteristics (age, gender, type of cancer, and palliative performance status), and to explore the correlation between cancer-related fatigue and pain, dyspnea, insomnia, and depression among palliative care patients. Methods: A cross-sectional descriptive observational study conducted at Comprehensive Cancer Center, King Fahad Medical City, Riyadh, Saudi Arabia. The study included cancer patients who received palliative care services from January 2016 to December 2021. Clinical data of study participants were retrospectively collected from Palliative Care department patient registry. Symptoms were assessed and scored using Edmonton Symptom Assessment Scale. Data analysis was performed using SPSS statistical software. Results: A total of 2616 patients were included in the study, 52.3% were females and 47.7% were males. The median age of study participants was 56 years (range: 2-101 years). Among all study population, the highest reported cancer type was gastrointestinal malignancy (33.5%), while the least was unknown primary malignancy (1.4%). With regards to Edmonton Symptom Assessment Scale, pain (86.4%) and fatigue (83%) were the highest reported symptom in comparison to constipation (17.3%) and insomnia (7.1%). Conclusion: Cancer-related fatigue is a prevalent and concerning issue among palliative care patients. It is essential that healthcare providers recognize the prevalence of fatigue among patients with life-limiting illnesses, assess patients for fatigue routinely, incorporate strategies for managing fatigue, work closely with affected individuals and their families in order to guide the establishment of a personalized care plan that addresses the patient's unique needs and preferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".