Examining the Effect of the Case Management Model on Patient Results in the Palliative Care of Patients With Cancer
Bibliographic record
Abstract
PURPOSE: We aimed to investigate the improvement in symptoms, quality of life, patient and family satisfaction with care, and direct costs resulting from a palliative care program based case management model. METHODS: The research was implemented at the Medical Oncology Clinic hospital of a University between September 2009 and September 2011. The research sample consisted of a total of 44 patients (22 control and 22 intervention group). The research tools were the Edmonton Symptom Diagnosis System, the Karnofsky Performance Scale, the EORTCQLQ-C30 Quality of Life Scale, a patient and family satisfaction form, and a patient cost record form. RESULTS: The difference between total symptom mean scores and the sub-dimension symptoms of pain, fatigue, nausea, depression, anxiety, lack of appetite, lethargy, well-being, dyspnea, and constipation post-hospitalization and post-discharge of patients in the control and experimental groups were found to be statistically significant (p < 0.05). The level of decrease in symptom severity in the experimental group patients was more than in the control group (p < 0.000). The satisfaction level of patients and family in the palliative care based case management service was higher than that for conventional service in the control group (p < 0.05). No statistical difference was detected between the experimental and control groups regarding health costs and duration of hospitalization (p > 0.05). CONCLUSION: We provided a better symptom control, improved the patient s quality of life (excluding physical and congnitive functions), and patient and family satisfaction levels were higher in the palliative care based case management intervention group, but direct health costs were not affected.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.001 |
| 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".