How should we assess renal function in neonates and infants?
Bibliographic record
Abstract
AIM: Review of current knowledge on assessing renal function in term and preterm neonates. METHODS: Literature review and analysis of own data. RESULTS: Prematurity, genetic, environmental and maternal factors may alter peak nephron endowment and life-long renal function. Nephrogenesis continues until 34-36 weeks of gestation, but it is altered with premature delivery. Variability of nephron endowment has a substantial impact on the clearance of renally excreted drugs. Postnatally, glomerular function rate (GFR) increases daily, doubles by two weeks, and slowly reaches full maturity at 18 months of age. Ideally, renal function biomarkers should be expressed as age-independent z-scores, and evidence suggests indexing these values to post-conceptual age rather than chronological age. Newborn and maternal serum creatinine correlate tightly for more than 72 hours after delivery, rendering this biomarker unsuitable for the assessment of neonatal renal function. Cystatin C does not cross the placenta and may be the preferred biomarker in the neonate. Here, we provide preliminary data on the natural evolution of the cystatin C eGFR in infancy. CONCLUSION: Cystatin C may be superior for GFR estimation in neonates, but the best approach to drug dosing of renally excreted drugs remains to be established.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".