Hour-Specific Total Serum Bilirubin Percentiles for Infants Born at 29–35 Weeks’ Gestation
Bibliographic record
Abstract
INTRODUCTION: As preterm infants are susceptible to hyperbilirubinemia, they require frequent close monitoring. Prior to initiation of phototherapy, hour-specific total serum bilirubin (TSB) percentile cut-points are lacking in these infants, which led to the current study. METHODS: A multi-site retrospective cohort study of preterm infants born between January 2013 and June 2017 was completed at 3 NICUs in Ontario, Canada. A total of 2,549 infants born at 290/7-356/7 weeks' gestation contributed 6,143 pre-treatment TSB levels. Hour-specific TSB percentiles were generated using quantile regression, further described by degree of prematurity, and among those who subsequently received phototherapy. RESULTS: Among all infants, at birth, hour-specific pre-treatment, TSB percentiles were 36.1 µmol/L (95% confidence interval [CI]: 34.3-39.3) at the 40th, 52.3 µmol/L (49.4-55.1) at the 75th, and 79.5 µmol/L (72.1-89.6) at the 95th percentiles. The corresponding percentiles were 39.3 μmol/L (35.9-43.2), 55.4 μmol/L (52.1-60.2), and 87.1 μmol/L (CI 70.5-102.4) prior to initiating phototherapy and 24.4 μmol/L (20.4-28.8), 35.3 μmol/L (31.1-41.5), and 52.0 μmol/L (46.1-62.4) among those who did not receive phototherapy. Among infants born at 29-32 weeks, pre-treatment TSB percentiles were 53.9 µmol/L (49.4-61.0) and 95.5 µmol/L (77.5-105.0) at the 75th and 95th percentiles, with respective values of 48.7 µmol/L (43.0-52.3), and 74.1 µmol/L (64.8-83.2) for those born at 33-35 weeks' gestation. CONCLUSION: Hour-specific TSB percentiles, derived from a novel nomogram, may inform how bilirubin is described in preterm newborns. Further research of pre-treatment TSB levels is required before clinical consideration.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".