Neonatal Acute Kidney Injury: A Survey of Neonatologists' and Nephrologists' Perceptions and Practice Management
Bibliographic record
Abstract
Background Neonatal acute kidney injury (AKI) occurs in 40 to 70% of critically ill neonatal intensive care admissions. This study explored the differences in perceptions and practice variations among neonatologists and pediatric nephrologists in diagnostic criteria, management, and follow-up of neonatal AKI. Methods A survey weblink was emailed to nephrologists and neonatologists in Australia, Canada, New Zealand, India, and the United States. Questions consisted of demographic and unit practices, three clinical scenarios assessing awareness of definitions of neonatal AKI, knowledge, management, and follow-up practices. Results Many knowledge gaps among neonatologists, and to a lesser extent, pediatric nephrologists were identified. Neonatologists were less likely to use categorical definitions of neonatal AKI (p < 0.00001) or diagnose stage 1 AKI (p < 0.00001) than pediatric nephrologists. Guidelines for creatinine monitoring for nephrotoxic medications were reported by 34% (aminoglycosides) and 62% (indomethacin) of respondents. Nephrologists were more likely to consider follow-up after AKI than neonatologists (p < 0.00001). Also, 92 and 86% of neonatologists and nephrologists, respectively, reported no standardization or infrastructure for long-term renal follow-up. Conclusion Neonatal AKI is underappreciated, particularly among neonatologists. A lack of evidence on neonatal AKI contributes to this variation in response. Therefore, dissemination of current knowledge and areas for research should be the priority.
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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 | 0.001 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".