Role of Integrative Oncology and Palliative Care Services in Improving Comfort Level and Compliance among Patients with Advanced Fungating Breast Cancer – Experience from a Rural Hospital of North Eastern India during the COVID-19 Pandemic
Bibliographic record
Abstract
Objectives: Malignant fungating breast cancers are a definite challenge to treatment due to limited knowledge and prevailing distressing symptoms. Various treatment protocols with regard to radiotherapy (RT) and chemotherapy have been reported in the literature but the knowledge of proper integration of these regimes with effective palliative care nursing care, particularly in resource poor settings, is not well understood. Hence, this study was undertaken to assess the change in the degree of comfort achieved before and after treatment of these fungating breast cancer wounds along with issues regarding compliance to such treatment in a peripheral rural medical college hospital. Materials and Methods: A total of 20 patients were selected from the medical records files of the RT department of North Bengal Medical College and Hospital who were registered and treated during the period 1 June 2019- 31 July 2021. Palliative care nursing of malodour, bleeding, maggots, pain and assessment of psychological parameters was done based on the 11-point revised Edmonton Symptom Assessment Scale at the beginning and at each subsequent follow-up visits. Patients were also individualised for receiving palliative RT, chemotherapy, surgery and hormone therapy based on their clinicodemographic profiles. Informed consent was taken from all patients and every treatment was in accordance with the ethical permissions as sought from the Institutional Ethics Committee. Statistical analysis was done based on descriptive statistics and SPSS version 22. Results: = 0.000). However, there was no significant correlation with palliative radiation dose and schedule as per spearman's correlation coefficient. The mean radiation dose was BED 48.56 Gy3 (EQD2 = 29.3 Gy3) and the median number of fractions used was 10. Almost 50% of patients were noncompliant to treatment and this might be attributed to the prevailing COVID-19 pandemic situation. There was maximum relief with regard to bleeding control (100%), malodour dissipation (76.9%) and control of maggots infestation (71%) and these results were also found to be strongly associated with treatment as per analysis done by Chi-square test of difference of proportions. Conclusion: Effective comfort can be achieved with proper judicious combination of palliative care nursing and other oncological treatment such as radiation, chemotherapy and surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".