Initial experience with delivery of palliative care to terminal cancer patients
Bibliographic record
Abstract
BACKGROUND/AIM: We investigated the symptoms and needs of terminal cancer patients in a region where formal palliative care is limited. Here we present the demographic features and symptoms of end-stage cancer patients living in a city in northern Turkey. MATERIALS AND METHODS: The study was conducted at Gaziosmanpaşa University (Tokat, Turkey), in 2011 and 2012. End-stage cancer patients admitted or referred by various departments to our outpatient pain unit were included. Demographic data, treatment histories, primary tumor sites, patient complaints, and symptom intensities measured using the Edmonton Symptom Assessment System Scale were prospectively entered into a database. RESULTS: A total of 107 patients (36 female and 71 male) were included. Gastrointestinal cancer was the most common form of cancer (43%), followed by genitourinary (25.3%) and lung cancer (15%). The most common symptom was fatigue (98.1%). The other symptoms (in decreasing order) were pain (92.5%), insomnia (92.5%), loss of appetite (76.6%), constipation (71%), dyspnea (63.6%), nausea (60.7%), cough (57.9%), and vomiting (48.6%). Eighty-six percent of the patients (n = 92) had metastases. Most lived in the city (59.8%) and 84 (78.5%) lived with their spouses. CONCLUSION: Patients were referred at the late stages of disease with pain as the principal presenting symptom. Family members were the principal caregivers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".