End-of-Life Treatments in Pediatric Patients at a Government Tertiary Cancer Center in India
Bibliographic record
Abstract
AIM: The primary objective of this study was to describe demographics and end-of-life treatments of children with cancer at a government tertiary cancer center in India. METHODS: A retrospective review was undertaken of medical charts of all children younger than 18 years, who died as inpatients while undergoing treatment at the pediatric oncology department between April and September 2016. Data were collected on demographics, diagnosis, treatments, survival, palliative care involvement, and symptoms at end of life. RESULTS: There were 44 pediatric oncology patients who died in the hospital during the study period. The most frequent diagnoses were hematological malignancies (n = 29). Tumor-specific treatment was given to 38/44 (86%) patients in the last 30 days of life, and 13 patients in the last day of life or 1 day before. Of all deaths, 23/44 (52%) occurred within 30 days of admission to the pediatric ward and 34/44 (77%) within 90 days. Of the 44 patients, 25 (57%) were referred to palliative care. The median number of days between referral and death was 14 (0-78) days. Frequent symptoms documented were bleeding (11/44), dyspnea (10/44), pain (7/44), seizures (7/44), and delirium (5/44), with each patient having one or more of these symptoms. Only patients with a palliative care referral received opioid analgesics or benzodiazepines at the end of life. CONCLUSIONS: This study highlights the demographics of suffering, death, and end-of-life care in children with cancer at a government tertiary cancer center in India.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".