Assessment of glomerular filtration rate in the neonate
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article answers the question of whether creatinine is the best biomarker for monitoring neonatal glomerular filtration rate (GFR) in view of recent advances in measuring neonatal renal function. RECENT FINDINGS: We rely largely on serum creatinine for the estimation of GFR in the newborn, even though creatinine is freely exchanged through the placenta. During the first few days of life, the serum creatinine reflects maternal renal function or the maternal creatinine. Back filtration of creatinine in preterm newborns is also a serious limitation. This review summarizes current knowledge on the prenatal and postnatal handling of creatinine as well as that of other, more novel biomarkers of GFR, such as cystatin C (CysC) and β-trace protein (BTP). Only small amounts of CysC cross the placenta, whereas BTP does not cross the placenta at all. However, BTP measurements are not widely available. Recent studies on renal volumetry are also discussed. SUMMARY: Currently, CysC may be the most suitable marker of neonatal renal function, but its availability is still limited, it is more costly, and the best method of reporting acute kidney injury and neonatal estimated GFR 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".