The Evaluation of the Symptom Clusters in Patients with the Diagnosis of Terminal Stage Cancer
Bibliographic record
Abstract
OBJECTIVES: Palliative care has been developing rapidly throughout the world. A substantial number of palliative care patients are advanced cancer patients. Terminal cancer patients suffer from groups of symptoms called symptom clusters, rather than from individual independent symptoms. The aim of this study is to evaluate the symptoms of terminal cancer patients retrospectively and to present the symptom clusters of these patients. METHODS: After ethical approval was obtained, a total of 113 (74 female, 39 male) patients with the diagnosis of the terminal stage cancer were retrospectively evaluated in Gaziosmanpasa University, Department of Anesthesiology and Reanimation between January 2011 and January 2013. Patient records were used to obtain medical history, physical examination findings, patient complaints, accompanying persons, primary cancer site, and metastasis sites. Symptoms such as fatigue, pain, vomiting, loss of appetite,insomnia, constipation, cough and dyspnea were assessed with the Edmonton Symptom Assessment System scale (0: None, 10: Worst possible). The symptom clusters were constructed using hierarchical symptom cluster analysis. RESULTS: The mean age was 64.51 ± 11.38 years. Patients were referred to our outpatient clinic from Departments of General Surgery, Emergency Medicine, Urology, Oncology, Ear-Nose-Throat, Thoracic Surgery, Internal Medicine and Neurosurgery. Fatigue was the most-detected symptom (98.2%). Three symptom clusters were identified: nausea-vomiting-loss of appetite-constipation, dyspnea-cough, and fatigue-pain-insomnia. CONCLUSION: Although palliative cancer patients were referred mainly with the symptom of pain, at least three symptom clusters were detected. The management of terminal stage cancer patients should focus on symptom clusters rather than individual symptoms.
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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 | 0.002 |
| 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 | 0.001 |
| 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".