Prevalence, factors associated and treatment outcome of hyperbilirubinaemia in neonates admitted to St Francis hospital, Nsambya, Uganda: a descriptive study
Bibliographic record
Abstract
BACKGROUND: With targeted management of neonatal hyperbilirubinaemia in high-income countries, there has been a drastic drop in both the prevalence and mortality. On the contrary, over two-thirds of the global burden of neonatal hyperbilirubinaemia is in Sub-saharan Africa and South East Asia with a high mortality risk of 16-35%. Neonatal hyperbilirubinaemia is not a leading global cause of neonatal mortality, however leads to irreversible neurological damage and death when managed poorly. Three-quarters of the babies admitted to the national referral hospital in Uganda had significant hyperbilirubinaremia; 16.6% of these babies died. We aimed at determining the prevalence, treatment outcome and describing factors associated with hyperbilirubinaemia in neonates admitted to St Francis hospital, Nsambya. METHODS: A cross sectional study was carried out. A total of 242 files of babies with a preliminary diagnosis of hyperbilirubinaemia were retrieved retrospectively. Relevant data was extracted from the files and analysed using STATA version 14.0. RESULTS: The prevalence of significant hyperbillirubinaemia was 22.7% (55/242). Seventy-seven percent of the babies admitted did not require treatment for hyperbilirubinaemia. No factors were found to be significantly associated with significant hyperbilirubinaemia. The case fatality for severe hyperbilirubinaemia was 20% (6/30); half of these babies had haemolytic disease of the newborn. CONCLUSION: Establishment of local guidelines will prevent unnecessary admissions and ensure timely treatment is administered. Longitudinal studies are required to discover factors associated with neonatal hyperbilirubinaemia in this region.
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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 | 0.001 | 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) | 0.001 | 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".