Association of Urine Platinum With Acute Kidney Injury in Children Treated With Cisplatin for Cancer
Bibliographic record
Abstract
Cisplatin is a chemotherapeutic agent highly excreted in urine and known to cause acute kidney injury (AKI). As AKI diagnosis by serum creatinine (SCr) is usually delayed, endeavors for finding early AKI biomarkers continue. This study aims to determine if urine platinum (UP) concentration 24 hours after cisplatin infusion is associated with AKI, and to evaluate the association between urine platinum and tubular damage biomarkers: neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1). Children treated with cisplatin in 12 Canadian centers (April 2013 to December 2017) were included. Urine from the morning after the first cisplatin infusion of the first or second cisplatin cycle was measured for urine platinum, NGAL, and KIM-1. SCr and serum electrolytes were used to detect AKI by either SCr elevation or urinary electrolyte wasting (potassium, magnesium, phosphate). The associations of urine platinum with AKI, NGAL, and KIM-1 were assessed. A total of 115 participants (54% boys, median age, 8.5 years; interquartile range, 4.0-13.4) were included, of which 29 (25%) and 105 (91%) developed AKI defined by SCr and electrolyte criteria, respectively. Higher urine platinum was associated with higher cisplatin dose (Spearman rho, 0.21) and with younger age (Spearman rho, -0.33). Urine platinum was not associated with postinfusion AKIor KIM-1, but was weakly associated with NGAL, particularly in participants without SCr AKI (Pearson's r, 0.22). Urine platinum may be a marker of mild tubular injury but is not likely to be a useful biomarker of clinically evident AKI.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".