Acute Kidney Injury, Fluid Overload, and Renal Replacement Therapy Differ by Underlying Diagnosis in Neonatal Extracorporeal Support and Impact Mortality Disparately
Bibliographic record
Abstract
INTRODUCTION: We aimed to characterize acute kidney injury (AKI), fluid overload (FO), and renal replacement therapy (RRT) utilization by diagnostic categories and examine associations between these complications and mortality by category. METHODS: To test our hypotheses, we conducted a retrospective multicenter, cohort study including 446 neonates (categories: 209 with cardiac disease, 114 with congenital diaphragmatic hernia [CDH], 123 with respiratory disease) requiring extracorporeal membrane oxygenation (ECMO) between January 1, 2007, and December 31, 2011. RESULTS: AKI, FO, and RRT each varied by diagnostic category. AKI and RRT receipt were most common in those neonates with cardiac disease. Subjects with CDH had highest peak %FO (51% vs. 28% cardiac vs. 32% respiratory; p < 0.01). Hospital survival was 55% and varied by diagnostic category (45% cardiac vs. 48% CDH vs. 79% respiratory; p < 0.001). A significant interaction suggested risk of mortality differed by diagnostic category in the presence or absence of AKI. In its absence, diagnosis of CDH (vs. respiratory disease) (OR 3.04, 95% CL 1.14-8.11) independently predicted mortality. In all categories, peak %FO (OR 1.20, 95% CL 1.11-1.30) and RRT receipt (OR 2.12, 95% CL 1.20-3.73) were independently associated with mortality. DISCUSSION/CONCLUSIONS: Physiologically distinct ECMO diagnoses warrant individualized treatment strategies given variable incidence and effects of AKI, FO, and RRT by category on mortality.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".