A Population Model of Time-Dependent Changes in Serum Creatinine in (Near)term Neonates with Hypoxic-Ischemic Encephalopathy During and After Therapeutic Hypothermia
Bibliographic record
Abstract
Abstract The objective was to apply a population model to describe the time course and variability of serum creatinine (sCr) in (near)term neonates with moderate to severe encephalopathy during and after therapeutic hypothermia (TH). The data consisted of sCr observations up to 10 days of postnatal age in neonates who underwent TH during the first 3 days after birth. Available covariates were birth weight (BWT), gestational age (GA), survival, and acute kidney injury (AKI). A previously published population model of sCr kinetics in neonates served as the base model. This model predicted not only sCr but also the glomerular filtration rate normalized by its value at birth (GFR/GFR0). The model was used to compare the TH neonates with a reference full term non-asphyxiated population of neonates. The estimates of the model parameters had good precision and showed high between subject variability. AKI influenced most of the estimated parameters denoting a strong impact on sCr kinetics and GFR. BWT and GA were not significant covariates. TH transiently increased $$\mathrm{sCr}$$ sCr in TH neonates over the first days compared to the reference group. Asphyxia impacted not only GFR, but also the $$\mathrm{sCr}$$ sCr synthesis rate. We also observed that AKI neonates exhibit a delayed onset of postnatal GFR increase and have a higher $$\mathrm{sCr}$$ sCr synthesis rate compared to no-AKI patients. Our findings show that the use of $$\mathrm{sCr}$$ sCr as marker of renal function in asphyxiated neonates treated with TH to guide dose selection for renally cleared drugs is challenging, while we captured the postnatal sCr patterns in this specific population. Graphical Abstract
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".