Serum Creatinine Patterns in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy
Bibliographic record
Abstract
INTRODUCTION: There is large variability in kidney function and injury in neonates with neonatal encephalopathy (NE) treated with therapeutic hypothermia (TH). Acute kidney injury (AKI) definitions that apply categorical approaches may lose valuable information about kidney function in individual patients. Centile serum creatinine (SCr) over postnatal age (PNA) may provide more valuable information in TH neonates. METHODS: Data from seven TH neonates and one non-TH-treated, non-NE control cohorts were pooled in a retrospective study. SCr centiles over PNA, and AKI incidence (definition: SCr ↑≥0.3 mg/dL within 48 h, or ↑ ≥1.5 fold vs. the lowest prior SCr within 7 days) and mortality were calculated. Repeated measurement linear models were applied to SCr trends, modeling SCr on PNA, birth weight or gestational age (GA), using heterogeneous autoregressive residual covariance structure and maximum likelihood methods. Findings were compared to patterns in the control cohort. RESULTS: Among 1,136 TH neonates, representing 4,724 SCr observations, SCr (10th-25th-50th-75th-90th-95th) PNA centiles (day 1-10) were generated. In TH neonates, the AKI incidence was 132/1,136 (11.6%), mortality 193/1,136 (17%). AKI neonates had a higher mortality (37.2-14.3%, p < 0.001). Median SCr patterns over PNA were significantly higher in nonsurvivors (p < 0.01) or AKI neonates (p < 0.001). In TH-treated neonates, PNA and GA or birth weight explained SCr variability. Patterns over PNA were significantly higher in TH neonates to controls (801 neonates, 2,779 SCr). CONCLUSIONS: SCr patterns in TH-treated NE neonates are specific. Knowing PNA-related patterns enable clinicians to better assess kidney function and tailor pharmacotherapy, fluids, or kidney supportive therapies.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".