Evaluation of the first day transcutaneous bilirubin (TcB) level as a predictor of hyperbilirubinemia in healthy term neonates
Bibliographic record
Abstract
Background: The readmission rate for neonatal jaundice has been increased in recent years. This has been attributed to shorter length of postpartum hospital stays without comprehensive follow-up. The purpose of this study was to determine the diagnostic value of pre-discharge transcutaneous bilirubin (TcB) levels for prediction of subsequent hyperbilirubinemia in healthy term neonates. Methods: Between October and December 2013, a total of 99 healthy term neonates born at the Babol Clinic Hospital of healthy mother were enrolled in a prospective cross sectional study. The TcB levels were measured in all enrolled neonates between 12 and 24 hours of age just before discharge. All newborns were followed-up for jaundice to 10th day. Newborns with clinical jaundice were recalled and their serum bilirubin levels were measured. Appropriate treatment was performed based on the Amirkola Children's Hospital Protocol. TcB levels were compared between the non-treatment and treatment groups. Results: The mean age of the TcB measuring was 17.5±2.6 hours. A total of 18.1% neonates (18 of 99) were developed severe hyperbilirubinemia (need for treatment). The mean of cutaneous bilirubin level in the non-treatment and treatment groups was 5.2±1.6mg/dl and 6.3±0.9 mg/dl, respectively. The cutaneous bilirubin level at cut-off 6 mg/dl was associated with 80.0% sensitivity and 63.0% specificity. The negative predictive value was 94.6%. Conclusion: Single TcB measurements at the first 24 hours predict hyperbilirubinemia with a reasonably high degree of accuracy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".