Newborn Bilirubin Screening for Preventing Severe Hyperbilirubinemia and Bilirubin Encephalopathy: A Rapid Review
Bibliographic record
Abstract
According to the 2004 American Academy of Pediatrics guideline on the management of hyperbilirubinemia, every newborn should be assessed for the risk of developing severe hyperbilirubinemia with the help of predischarge total serum bilirubin or transcutaneous bilirubin measurements and/or assessments of clinical risk factors. The aim of this rapid review is 1) to review the evidence for 1) predicting and preventing severe hyperbilirubinemia and bilirubin encephalopathy, 2) determining the efficacy of home/community treatments (home phototherapy) in the prevention of severe hyperbilirubinemia, and 3) non-invasive/transcutaneous methods for estimating serum bilirubin level. METHODS: In this rapid review, studies were identified through the Medline database. The main outcomes of interest were severe hyperbilirubinemia and encephalopathy. A subset of articles was double screened and all articles were critically appraised using the SIGN and AMSTAR checklists. This review investigated if systems approach is likely to reduce the occurrence of severe hyperbilirubinemia. RESULTS: Fifty-two studies met the inclusion criteria. Included studies assessed the association between bilirubin measurement early in neonatal life and the subsequent development of severe hyperbilirubinemia and chronic bilirubin encephalopathy/kernicterus. It was observed that, highest priority should be given to (i) universal bilirubin screening programs; (ii) implementation of community and midwife practice; (iii) outreach to communities for education of prospective parents; and (iv) development of clinical pathways to monitor, evaluate and track infants with severe hyperbilirubinemia. CONCLUSIONS: We found substantial observational evidence that severe hyperbilirubinemia can be accurately predicted and prevented through universal bilirubin screening. So far, there is no evidence of any harm.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".