Urine biomarkers of acute kidney injury in noncritically ill, hospitalized children treated with chemotherapy
Bibliographic record
Abstract
BACKGROUND: Cisplatin (Cis), carboplatin (Carb), and ifosfamide (Ifos) are common nephrotoxic chemotherapies. Biomarkers of tubular injury may allow for early acute kidney injury (AKI) diagnosis. PROCEDURE: We performed a two-center (Canada, United States) pilot study to prospectively measure serum creatinine (SCr), urine neutrophil gelatinase-associated lipocalin (NGAL), and interleukin-18 (IL-18) in children receiving Cis/Carb (27 episodes), Ifos (30 episodes), and in 15 hospitalized, nonchemotherapy patients. We defined AKI using the Kidney Disease Improving Global Outcomes (KDIGO) definition. We compared postchemotherapy infusion NGAL and IL-18 concentrations (immediate postdose to 3 days later) to pre-infusion concentrations. We calculated area under the receiver operating characteristic curve (AUC) for postinfusion biomarkers to discriminate for AKI. RESULTS: Prechemotherapy infusion NGAL and IL-18 concentrations were not higher than nonchemotherapy control concentrations. Increasing chemotherapy dose was associated with increasing postinfusion (0-4 hr after infusion) NGAL (P < 0.05). Post-Ifos, immediate postdose, and daily postdose NGAL and IL-18 were significantly higher than pre-infusion biomarker concentrations (P < 0.05), during AKI episodes. NGAL and IL-18 did not rise significantly after Cis-Carb infusion, relative to predose concentrations (P > 0.05). NGAL and IL-18 measured immediately after Ifos infusion discriminated for AKI with AUCs is 0.80 (standard error = 0.13) and 0.73 (standard error = 0.16), respectively. NGAL and IL-18 were not diagnostic of Cis-Carb-associated AKI. When AUCs were adjusted for age, all biomarker AUCs (Cis-Carb and Ifos) improved. CONCLUSION: Urine NGAL and IL-18 show promise as early AKI diagnostic tests in children treated with ifosfamide and may have a potential role in drug toxicity monitoring.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".