Burden of severe neonatal jaundice: a systematic review and meta-analysis
Bibliographic record
Abstract
CONTEXT: To assess the global burden of late and/or poor management of severe neonatal jaundice (SNJ), a common problem worldwide, which may result in death or irreversible brain damage with disabilities in survivors. Population-based data establishing the global burden of SNJ has not been previously reported. OBJECTIVE: Determine the burden of SNJ in all WHO regions, as defined by clinical jaundice associated with clinical outcomes including acute bilirubin encephalopathy/kernicterus and/or exchange transfusion (ET) and/or jaundice-related death. DATA SOURCES: PubMed, Scopus and other health databases were searched, without language restrictions, from 1990 to 2017 for studies reporting the incidence of SNJ. STUDY SELECTION/DATA EXTRACTION: Stratification was performed for WHO regions and results were pooled using random effects model and meta-regression. RESULTS: Of 416 articles including at least one marker of SNJ, only 21 reported estimates from population-based studies, with 76% (16/21) of them conducted in high-income countries. The African region has the highest incidence of SNJ per 10 000 live births at 667.8 (95% CI 603.4 to 738.5), followed by Southeast Asian, Eastern Mediterranean, Western Pacific, Americas and European regions at 251.3 (132.0 to 473.2), 165.7 (114.6 to 238.9), 9.4 (0.1 to 755.9), 4.4 (1.8 to 10.5) and 3.7 (1.7 to 8.0), respectively. The incidence of ET per 10 000 live births was significantly higher for Africa and Southeast Asian regions at 186.5 (153.2 to 226.8) and 107.1 (102.0 to 112.5) and lower in Eastern Mediterranean (17.8 (5.7 to 54.9)), Americas (0.38 (0.21 to 0.67)), European (0.35 (0.20 to 0.60)) and Western Pacific regions (0.19 (0.12 to 0.31). Only 2 studies provided estimates of clear jaundice-related deaths in infants with significant jaundice [UK (2.8%) and India (30.8%). CONCLUSIONS: Limited but compelling evidence demonstrates that SNJ is associated with a significant health burden especially in low-income and middle-income countries.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".