Palliative Care for Newborns in India: Patterns of Care in a Neonatal Palliative Care Program at a Tertiary Government Children’s Hospital
Bibliographic record
Abstract
Neonatal palliative care is a specialized area within children's palliative care, which focusses on the needs of infants with life-limiting or life-threatening conditions. Nearly one quarter of global neonatal deaths occur in India, where neonatal palliative care evidence is limited. This study describes the development and implementation of a neonatal palliative care program within a neonatal intensive care unit (NICU) at a government hospital, describing the implementing an 8-month pilot palliative care program for neonates, including the patterns of care, and barriers and enablers of success. The hospital-based palliative care team included trained pediatric palliative care physicians, a nurse, and a counselor. There was a steady increase in monthly referrals. There were 110 referrals in total, including 89 (81%) deaths and 18 (16%) babies were alive at the time of final follow-up, 10 months after the pilot program was completed. The program addressed physical symptoms, including providing morphine, as well as psychosocial and spiritual concerns of families. A model of hospital-based palliative care for neonates can be implemented within NICUs in tertiary government hospitals in India. Neonatal palliative care programs should include partnerships with charitable organizations to support implementation costs and provide palliative care training, mentorship, and capacity-building support.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".