Early prediction of hyperbilirubinemia by neonatal hour-specific bilirubin nomogram
Bibliographic record
Abstract
Objective To design the hour-specific bilirubin nomogram of healthy term and near-term newborns and assess its predictive ability to screen risks of sequent hyperbilirubinemia.Methods Transcutaneous bilirubin(TCB) was measured for healthy infants(gestational age≥35 weeks and birth weight≥2 000 g) from birth to 7 days.Infants whose TCB≥250 μmol·L-1 were detected for bilirubin with microdetermination.We designed the hour-specific bilirubin nomogram with these data.Newborns were divided into 4 groups based on the predischarge bilirubin zone(≤40th,-75th,-95th,and 95th percentile defined as low,low-intermediate,high-intermediate and high risk zones on the hour-specific bilirubin nomogram) to predict subsequent hyperbilirubinemia.Results Four thousand and four hundred sixty-two infants with 27 271 hour-specific bilirubin data were enrolled into the study.Before discharge,total serum bilirubin(TSB) values of 5.2%(233/4 462) infants were in the high-risk zone;of them,48.9%(114/233) remained in that zone [likelihood ratio(LR) 9.5,sensitivity 26.7%;specificity 97.1%,probability 48.9%] post discharge.Before discharge,TSB values of 23.2%(1 034/4 462) infants were in the high-risk and intermediate-risk zone(sensitivity 78.9%,specificity 82.5%).The predischarge TSB values in 41.3%(1 845/4 462) infants were in the low-risk zone and there was no measurable risk for hyperbilirubinemia(LR 0,sensitivity 100%,specificity 45.5%,probability 0).The receiver operating characteristic(ROC) curves were drawn to assess the predictive abilities of the predischarge bilirubin level and prediction model that was combined with gestational age.The areas under the ROC curves were 0.870 and 0.908,respectively.Conclusions The study showed good predictivity of hour-specific TSB to the risk of hyperbilirubinemia of newborns.Gestational age improved prediction of subsequent hyperbilirubinemia compared with early TSB levels alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".