Quality of Life and Neuropathic Pain in Hospitalized Cancer Patients: A Comparative Analysis of Patients in Palliative Care Wards Versus Those in General Wards.
Bibliographic record
Abstract
CONTEXT: While the survival of cancer patients is prolonged due to the development of new treatment strategies and advancing technologies, the prevalence of symptoms such as neuropathic pain affecting the quality of life is also increasing. AIMS: The aim of this study is to determine the relationship between neuropathic pain (NP) and quality of life in hospitalized cancer patients and to compare patients in general wards and those in palliative care wards in terms of NP and quality of life. SUBJECTS AND METHODS: A total of 156 patients, 53 cancer patients hospitalized in the palliative care unit and 103 cancer patients hospitalized in general wards, were included in the study. The Douleur Neuropathic 4 test was used for NP assessment, and the Edmonton Symptom Assessment System (ESAS), Hospital Anxiety and Depression Scale (HAD), Brief Fatigue Inventory (BFI), and Short Form of Brief Pain Inventory (SF-BPI) were used for assessing pain characteristics and their effects on quality of life. RESULTS: < 0.05). CONCLUSIONS: Since there was a homogeneous distribution among the groups in terms of both cancer treatment and pain management, we directly related the deterioration of the patients' quality of life to NP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".