Impact of Symptom Distress on the Quality of Life of Oncology Palliative Care Patients: A Portuguese Cross-Sectional Study
Bibliographic record
Abstract
Introduction: Uncontrolled symptoms are widely recognized as one of the main challenges in oncology palliative care patients. The central aim of palliative care is to improve the patient’s quality of life. In recent years, there has been a growing use of patient-reported outcome measures in palliative care, particularly to evaluate symptoms, quality of care, and well-being. Aim: To evaluate the sociodemographic and clinical profile, symptom distress, and perceived quality of life in oncology palliative care patients admitted to a specialized palliative care unit in Portugal. Methods: This study was cross-sectional, descriptive, and correlational, carried out in the inpatient setting of the palliative care unit at a tertiary oncology hospital (at admission). The evaluated protocol included a sociodemographic and clinical questionnaire, as well as two measurement instruments: the Edmonton Symptom Assessment Scale (ESAS) and the Palliative Care Outcome Scale (POS), both filled out by the patients. Data analysis was conducted using IBM SPSS® Statistics version 25.0, with a significance level set at 5% (p < 0.05). Results: The majority of participants in this sample were male (61.7%), with a mean age of around 72 years. More than half of the patients admitted (n = 34; 56.7%) were being monitored in outpatient care. Digestive and head and neck cancers were the most commonly found in the sample (41.7% and 20%, respectively). A significant correlation was found between high symptom intensity and poorer quality of life and care (p < 0.01). This association was particularly pronounced for symptoms such as pain, weakness, depression, anxiety, and anorexia. Conclusions: This study revealed a positive correlation between overall symptom severity and a perceived deterioration in quality of life, well-being, and quality of care. Future studies should consider utilizing alternative assessment tools for evaluating symptoms and quality of care. Additionally, including non-cancer palliative patients in similar studies may provide further valuable insights.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".