Acute kidney injury, fluid balance, and continuous renal replacement therapy in children and neonates treated with extracorporeal membrane oxygenation
Bibliographic record
Abstract
Abstract Extracorporeal membrane oxygenation (ECMO) is a lifesaving therapy used primarily for reversible cardiopulmonary failure across the lifespan. Mortality from multiple organ failure on ECMO is high, and unfortunately, complications such as acute kidney injury (AKI) and disorders of fluid balance such as fluid overload (FO) necessitating continuous renal replacement therapy (CRRT) are also common. The largest series of AKI, FO and ECMO related outcomes has been published by the Kidney Interventions During Membrane Oxygenation (KIDMO) multicenter study, which demonstrated patients with AKI and FO have worse outcomes, corroborating with findings from previous single center studies. There are multiple ways to perform CRRT during ECMO, but integration of a CRRT machine in series is the most common approach in neonates and children. The optimal timing of when to initiate CRRT, and how fast to remove fluid during ECMO remain unknown, and there is an urgent need to design studies with these research questions in mind. The disposition and clearance of drugs on ECMO also require urgent study, as drugs metabolism not only is disproportionately affected by the presence of AKI and FO, but also by CRRT prescription and the rate of fluid removal. In this review, we discuss the contemporary epidemiology and outcomes of AKI and FO during ECMO, as well as the use of concurrent CRRT and highlight evidence gaps as a research map.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".