Impact of Community Palliative Care on Quality of Life among Cancer Patients in Bangladesh
Bibliographic record
Abstract
Cancer, a leading cause of mortality worldwide, is often diagnosed at late stages in low- and middle-income countries, resulting in preventable suffering. When added to standard oncological care, palliative care may improve the quality of life (QOL) of these patients. A longitudinal observational study was conducted from January 2020 to December 2021. Thirty-nine cancer patients were enrolled in the Compassionate Narayanganj community palliative care group (NPC), where they received comprehensive palliative care in addition to oncological care. Thirty-one patients from the Dept. of Oncology (DO) at BSMMU received standard oncological care. In contrast to the DO group, the NPC group had a higher percentage of female patients, was older, and had slightly higher levels of education. At 10 to 14 weeks follow-up, a significant improvement in overall QOL was observed in the NPC group (p = 0.007), as well as in the psychological (p = 0.003), social (p = 0.002), and environmental domains (p = 0.15). Among the secondary outcomes, the palliative care group had reduced disability and neuropathic pain scores. Additionally, there were statistically significant reductions in pain, drowsiness, and shortness of breath, as well as an improvement in general wellbeing, based on the results of the Edmonton Symptom Assessment Scale—Revised. At the community level in Bangladesh, increased access to palliative care may improve cancer patient outcomes such as QOL and symptom burden.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".