Tubular injury in diabetic ketoacidosis: Results from the diabetic kidney alarm study
Bibliographic record
Abstract
OBJECTIVE: Glomerular injury is a recognized complication of diabetic ketoacidosis (DKA), yet the tubular lesions are poorly understood. The aim of this prospective study was to evaluate the presence and reversibility of tubular injury during DKA in children with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Blood and urine samples were collected from 40 children with DKA (52% boys, mean age 11 ± 4 years, venous pH 7.2 ± 0.1, glucose 451 ± 163 mg/dL) at three timepoints: 0-8 and 12-24 h after starting insulin, and 3 months after discharge. Mixed-effects models evaluated the changes in tubular injury markers over time (neutrophil gelatinase-associated lipocalin [NGAL], kidney injury molecule 1 [KIM-1], and interleukin 18 [IL-18]). We also evaluated the relationships among the tubular injury biomarkers, copeptin, a vasopressin surrogate, and serum uric acid (SUA). RESULTS: Serum NGAL, KIM-1, and IL-18 were highest at 0-8 h (306.5 ± 45.9 ng/mL, 128.9 ± 10.1 pg/mL, and 564.3 ± 39.2 pg/mL, respectively) and significantly decreased over 3 months (p = 0.03, p = 0.01, and p < 0.001, respectively). There were strong relationships among increases in copeptin and SUA and rises in tubular injury biomarkers. At 0-8 h, participants with acute kidney injury (AKI) [17%] showed significantly higher concentrations of tubular injury markers, copeptin, and SUA. CONCLUSIONS: DKA was characterized by tubular injury, and the degree of injury associated with elevated copeptin and SUA. Tubular injury biomarkers, copeptin and SUA may be able to predict AKI in 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".