Treatment practices and implementation of guidelines for hyperbilirubinemia and rebound hyperbilirubinemia
Bibliographic record
Abstract
BACKGROUND: Hyperbilirubinemia (HB), defined as elevated total serum bilirubin (TSB) levels, commonly affects neonates and requires prompt treatment to prevent neurological complications. Up to 10%of neonates experience rebound hyperbilirubinemia (RHB), requiring re-initiation of treatment. Unfortunately, treatment guidelines lack practical recommendations surrounding subthreshold phototherapy, treatment termination, and RHB investigations. We examined local management practices for HB and RHB treatment in a well newborn nursery. As a secondary aim, we investigated the association between treatment practices and RHB rates. METHODS: Retrospective chart review identified neonates treated for hyperbilirubinemia between January 2015 and December 2019 during their birth hospitalization at a tertiary care centre. Standardized data collection sheets were used to record treatment parameters. RESULTS: Over the 5-year period, there were 9683 births and 305 (3.15%) neonates received phototherapy. Of the treated cases, 20-25%were subthreshold to practice guideline values. Upon treatment termination 25-55%of cases had TSB levels within 3 mg/dL, which may increase the risk of RHB. In our cohort, 20.3%of treated cases experienced one episode of RHB and 3.9%experienced two episodes of RHB. Although clinicians evaluated neonates for RHB 0-12 hours following treatment termination prior to discharge, many cases were identified in outpatient settings and required re-admission for phototherapy. CONCLUSION: When managing HB and RHB, treatment practices such as when to terminate treatment in relation to threshold values, and timing of RHB investigations, are largely inconsistent amongst clinicians. Future studies are required to better understand the landscape of hyperbilirubinemia treatment beyond initiation of phototherapy.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".