Delivering Hospital-Based Pediatric Palliative Care: The Symptoms, Interventions, and Outcomes for Children With Cancer in Bangladesh
Bibliographic record
Abstract
PURPOSE: The majority of pediatric cancer deaths occur in low- and middle-income countries (LMICs). Pediatric palliative care (PPC) focuses on relieving physical, psychosocial, and spiritual suffering throughout the continuum of cancer care and is considered integral to cancer care for children in all settings. There is limited evidence from LMICs about the characteristics, symptoms, and outcomes of children with cancer who receive PPC, which is needed to define the global need and guide the development of these services. METHODS: This retrospective review of clinical records of children who received PPC was conducted during a pilot project (January 2014-August 2015) that implemented a PPC team at a tertiary hospital in Dhaka, Bangladesh. Clinical data on diagnosis, symptoms, treatment status, deaths, and key palliative care interventions were collected and analyzed using descriptive statistics. RESULTS: There were 200 children who received PPC during the pilot project. The most common diagnoses were acute lymphoblastic leukemia (62%) and acute myeloid leukemia (11%). Psychosocial support for children (n = 305; 53%) and management of physical symptoms (n = 181; 31%) were the most common types of interventions provided. The most frequently recorded symptoms were pain (n = 60; 30%), skin wounds (n = 16; 8%), and weakness (n = 9; 5%). The most common medications prescribed were morphine (n = 32) and paracetamol (n = 21). CONCLUSION: A hospital-based PPC service addresses pain and symptom concerns as well as psychosocial needs for children with cancer and their families in a setting where resources are limited. Health care facilities should incorporate palliative care into the care of children with cancer to address the needs of children and their families.
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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 | 0.004 |
| 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".