Symptoms in Advanced Cancer Patients in a Greek Hospital: a Descriptive Study
Bibliographic record
Abstract
Background: Advanced cancer patients experience several physical or psychological symptoms which require palliative care for alleviation. Purpose: To assess the prevalence and intensity of symptoms among cancer patients receiving palliative care in a Greek hospital and to examine the association between reported symptoms and social clinical and demographic characteristics. Material-methods: This descriptive research was conducted during a sixmonth period using a convenient sample of 123 advanced cancer patients. All participants were assessed for their symptoms using the Edmonton Symptom Assessment System (ESAS) with a questionnaire covering demographic and clinical characteristics. Results: The mean age was 63.8± 10.8 years, with lung and breast (58.5% and 11.4%, respectively) as the most common primary cancer types. The most severe symptoms were fatigue, sleep disturbance, dyspnea, depression and anxiety. Negative correlations were revealed between age and the following symptoms: pain (r = -0.354, p = 0.001), fatigue (r = -0.280, p = 0.002), nausea (r = -0.178, p = 0.049), anorexia (r = -0.188, p = 0.038), dyspnea (r = -0.251, p = 0.005), and depression (r = -0.223, p = 0.013). Advanced breast cancer patients scored higher in pain, fatigue and dyspnea compared to those with other cancers. Conclusions: Hospitalized cancer patients in Greece experience several symptoms during the last months of their life. These are influenced by demographic characteristics. Appropriate interventions are strongly advised with appropriate recognition and evaluation of symptoms by health professionals.
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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 | 0.001 |
| 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".