Demographic and Clinical profile of Neonates admitted in a NICU of a Tertiary Care Hospital in Bangladesh
Bibliographic record
Abstract
Background: Neonatal morbidity and mortality remain major public health challenges in Bangladesh, particularly among critically ill neonates requiring intensive care. Identifying the clinical profile and outcomes of neonates admitted to tertiary care facilities is essential for improving neonatal survival and guiding preventive strategies. Methods: This cross-sectional study was conducted in the Department of Neonatal Medicine, Bangladesh Shishu Hospital and Institute, Dhaka, from 1st January 2023 to 30th June 2023. A total of 382 neonates were admitted to the Neonatal Intensive Care Unit (NICU) during this period were included. Data on demographic characteristics, morbidity patterns and outcomes were collected and analyzed using SPSS version 25. Results: Among 382 neonates admitted to the Neonatal Intensive Care Unit (NICU), males predominated (63.6%). Most were term infants (81.7%) and delivered by normal vaginal delivery (57.6%). Birth asphyxia (41.9%) was the leading cause of morbidity, followed by preterm low birth weight (18.3%) and neonatal sepsis (13.1%). The overall mortality rate was 41.4%, with birth asphyxia (54.4%) being the primary cause of death. Most admissions occurred within 24–72 hours of life (55.2%). Conclusion: Birth asphyxia, neonatal sepsis and prematurity were identified as the major causes of neonatal morbidity and mortality in this tertiary care setting. Northern International Medical College Journal Vol. 16 No. 1-2 July 2024-January 2025, Page 740-744
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".