Cystatin C-Defined AKI in Children Treated with Cisplatin
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Bibliographic record
Abstract
Background: Cisplatin chemotherapy causes acute kidney injury (AKI). Early AKI detection and mitigation may prevent long-term sequelae. Serum Cystatin C (CysC) may be an early AKI biomarker compared to SCr and is not impacted by muscle mass. We: 1) compared AKI defined by acute CysC change (CysC-AKI) to SCr-defined AKI (SCr-AKI) in children treated with cisplatin; 2) evaluated the relation of a) urine neutrophil gelatinase-associated lipocalin [NGAL] and kidney injury molecule-1 [KIM-1] with CysC-AKI and SCr-AKI and b) of CysC with SCr-AKI. Methods: 12-centre prospective study of 159 children receiving cisplatin. Exclusions: CKD, missing CysC or baseline SCr. At early cisplatin infusions (1st or 2nd cisplatin cycle), SCr, CysC, urine NGAL and KIM-1 were measured pre-/post-infusion and at discharge. Outcomes: KDIGO SCr-AKI (yes/no). CysC-AKI was ≥1.5x rise from baseline or CysC-GFR<35ml/min/1.73m2. We calculated kappa statistic for agreement of AKI definitions, Mann-Whitney U test to compare biomarker levels by AKI, and area under the curve (AUC) for a) NGAL/KIM-1 to predict CysC-AKI and SCr-AKI and b) for CysC to predict SCr-AKI. Results: 154 children included (51% male, median age [IQR] 5.6 [2.4-11.7] years). SCr-AKI 7/154 (5%), CysC-AKI 25/154 (16%), 83% agreement (kappa=0.13, p=0.025). There was no significant difference in NGAL or KIM-1 concentrations by SCr-AKI or CysC-AKI at all time points (Figure1). Pre- and post-infusion NGAL performed similarly to predict SCr-AKI and CysC-AKI (Figure1). KIM-1 poorly predicted SCr-AKI and CysC-AKI (Figure1). Pre-infusion CysC had the highest AUC for predicting SCr-AKI (AUC 0.56).Figure 1:: Comparison of urinary biomarkers (NGAL/KIM-1) by SCr-AKI and CysC-AKI in children treated with clsplatin.Conclusions: CysC-AKI had a low level of agreement with SCr-AKI in children treated with cisplatin. CysC was not a strong predictor of SCr-AKI in this population. Future studies with more measurement time points are needed to determine if this is possibly due to earlier rise of CysC compared to SCr. NGAL and KIM-1 did not strongly predict AKI by either definition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it