Diagnosis (by p-RIFLE and KDIGO) and Risk Factors of Acute Kidney Injury in Pediatric Diabetic Ketoacidosis: A Retrospective Study
Bibliographic record
Abstract
Background: There are two criteria to diagnose and stage acute kidney injury (AKI) in children: pediatric-Risk, Injury, Failure, Loss (p-RIFLE) and Kidney Disease Improving Global Outcomes (KDIGO). This study aims to find out the extent of agreement in diagnosis (by p-RIFLE and KDIGO) and risk factors of AKI in pediatric diabetic ketoacidosis (DKA). Materials and Methods: A retrospective cohort study involving children aged ≤15 years with DKA was conducted between January 2014 and December 2022. Those with inborn errors of metabolism, septic shock, and urinary tract disease were excluded. The primary outcome was the extent of agreement in diagnosis of AKI by p-RIFLE and KDIGO. The secondary outcomes were staging agreement, risk factors, complications (hypoglycemia, hypokalemia, and cerebral edema), time to resolution of DKA, and hospital and pediatric intensive care units (PICU) stay. Results: Data from 161 patients were collected. Mean (SD) age was 8.6 (3.7) years. Good agreement between p-RIFLE and KDIGO criteria for diagnosis of AKI was noted at admission (Kappa = 0.71, p ≤ 0.001), at 24 hours (Kappa = 0.73, p ≤ 0.001) and discharge (Kappa = 0.60, p ≤ 0.001), and for the staging of AKI at admission (Kappa = 0.81, p ≤ 0.001) at 24 hours (Kappa = 0.75, p ≤ 0.001) and discharge (Kappa = 0.48, p ≤ 0.001). On multivariate analysis, age (≤5 years: aOR = 3.03, 95% CI 1.04-8.79) is an independent risk factor for AKI at discharge by KDIGO. Cerebral edema (n = 6, 3.7%), hypoglycemia (n = 66, 41%), and hypokalemia (n = 59, 36.6%) were noted. Resolution and stay in PICU and hospitals were longer for patients with AKI. Conclusion: p-RIFLE and KDIGO criteria showed good agreement in diagnosis and staging of AKI in pediatric DKA.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".