Is facility based neonatal care in low resource setting keeping pace? A glance at Uganda’s National Referral Hospital
Bibliographic record
Abstract
OBJECTIVES: To identify reasons for neonatal admission and death with the aim of determining areas needing improvement. METHOD: A retrospective chart review was conducted on records for neonates admitted to Mulago National Referral Hospital Special Care Baby Unit (SCBU) from 1(st) November 2013 to 31(st) January 2014. Final diagnosis was generated after analyzing sequence of clinical course by 2 paediatricians. RESULTS: A total of 1192 neonates were admitted. Majority 83.3% were in-born. Main reasons for admissions were prematurity (37.7%) and low APGAR (27.9%).Overall mortality was 22.1% (Out-born 33.6%; in born 19.8%). Half (52%) of these deaths occurred in the first 24 hours of admission. Major contributors to mortality were prematurity with hypothermia and respiratory distress (33.7%) followed by birth asphyxia with HIE grade III (24.6%) and presumed sepsis (8.7%). Majority of stable at risk neonates 318/330 (i.e. low APGAR or prematurity without comorbidity) survived. Factors independently associated with death included gestational age <30 weeks (p 0.002), birth weight <1500g (p 0.007) and a 5 minute APGAR score of < 7 (p 0.001). Neither place of birth nor delayed and after hour admissions were independently associated with mortality. CONCLUSION AND RECOMMENDATIONS: Mortality rate in SCBU is high. Prematurity and its complications were major contributors to mortality. The management of hypothermia and respiratory distress needs scaling up. A step down unit for monitoring stable at risk neonates is needed in order to decongest SCBU.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".