An Evaluation of Palliative Care Service Effect in Patients With Cancer Diagnosis; Comparison in Terms of The Symptom Level and Care Satisfaction
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the effect of palliative care on the symptom level assessment and satisfaction of patients diagnosed with cancer. METHODS: The study was carried out with 60 cancer patients who received service at a palliative care center (PCC) and 59 cancer patients who received general care services at a public hospital. The effect of the services provided at the 2 hospitals was evaluated using the Edmonton Symptom Assessment System and the European Organisation for Research and Treatment of Cancer In-patient Satisfaction with Care Questionnaire. The data were analyzed to determine number and percentage distributions, the significance of differences between 2 peers, and 2-way analysis of variance in repetitive measurements. RESULTS: It was determined that the symptom severity of the PCC patients was greater. In a 1 week interval, greater improvement was observed in all of the symptoms of the patients who received general care, and the evaluation revealed a statistically significant difference between the hospitals in terms of fatigue, nausea, and dyspnea (p<0.05). However, the mean satisfaction of the patients who received services at the PCC was higher, and the difference in the general satisfaction level between hospitals was statistically significant (p<0.05). CONCLUSION: The palliative care provided to cancer patients at the PCC was less effective in reducing symptom levels compared with the results from patients of general care at a public hospital, but provided greater patient satisfaction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".