Transforming cancer care: The vital role of palliative support
Bibliographic record
Abstract
Cancer remains a leading cause of mortality globally, with the increasing cancer burden in both developed and developing countries, including India. Palliative care, which focuses on alleviating the symptoms and improving the quality of life for patients, plays a crucial role in cancer management. Despite its significance, the integration of palliative care into cancer treatment in India faces numerous challenges, particularly in rural areas where healthcare access is limited. This study aims to examine the role of palliative care in the comprehensive management of cancer patients, particularly in India. It seeks to explore the specific needs of elderly cancer patients, assess common barriers to effective palliative care, and evaluate the impact of home-based palliative care models in improving patient outcomes. Data was collected through a combination of literature review and patient surveys, focusing on cancer care and palliative needs in India. Assessment tools such as the Edmonton Symptom Assessment Scale (ESAS) and the Palliative Performance Scale (PPS) were employed to evaluate symptom burden and prognosis. The study also analysed operational strategies for implementing home-based palliative care and the role of multidisciplinary teams in enhancing care delivery. The study found that palliative care significantly improves the quality of life for cancer patients by addressing their physical, emotional, and social needs. Home-based palliative care emerged as an effective model, particularly for patients in rural areas, where access to specialised healthcare is limited. Challenges such as a lack of trained personnel, inadequate awareness of palliative care, and logistical difficulties in rural settings were highlighted. The use of assessment tools such as ESAS and PPS helped in better symptom management and tailoring care to individual needs. Palliative care plays a critical role in the holistic management of cancer patients, particularly in resource-limited settings like India. The study underscores the need for more widespread integration of palliative care into the healthcare system, with a focus on home-based care and interdisciplinary collaboration. Increased awareness, training, and policy support are essential for improving access to and the quality of palliative care services, enhancing the well-being of cancer patients across the country.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".