Novel extracorporeal treatment for severe neonatal jaundice: a mathematical modelling study of allo-hemodialysis
Bibliographic record
Abstract
Severe Neonatal Jaundice (SNJ) causes long-term neurocognitive impairment, cerebral palsy, auditory neuropathy, deafness, or death. We developed a mathematical model for allo-hemodialysis as a potential blood purification method for the treatment of SNJ in term or near-term infants. With allo-hemodialysis (allo-HD), the neonate’s blood flows through hollow fibers of a miniature 0.075 m 2 hemodialyzer, while the blood of a healthy adult (“buddy”) flows counter-currently through the dialysate compartment. We simulated the kinetics of unconjugated bilirubin in allo-hemodialysis with neonate blood flow rates of 12.5 and 15 mL/min (for a 2.5 kg and 3.5 kg neonate, respectively), and 30 mL/min for the buddy. Bilirubin production rates in neonate and buddy were set to 6 and 3 mg/kg/day, respectively. Buddy bilirubin conjugation rate was calculated to obtain normal steady-state bilirubin levels. Albumin levels were set to 1.1, 2.1, 3.1 g/dL for the neonate and 3.3 g/dL for the buddy. Model simulations suggest that a 6-h allo-hemodialysis session could reduce neonatal bilirubin levels by > 35% and that this modality would be particularly effective with low neonatal serum albumin levels. Due to the high bilirubin conjugation capacity of an adult’s healthy liver and the larger distribution volume, the buddy’s bilirubin level increases only transiently during allo-hemodialysis. Our modelling suggests that a single allo-hemodialysis session may lower neonatal unconjugated bilirubin levels effectively. If corroborated in ex-vivo , animal, and clinical studies, this bilirubin reduction could lower the risks associated with SNJ, especially kernicterus, and possibly avoiding the morbidity associated with exchange transfusions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".