High incidence of acute kidney injury during chemotherapy for childhood acute myeloid leukemia
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Childhood acute myeloid leukemia (AML) is a rare and heterogeneous disease. Pediatric data on the epidemiology of acute kidney injury (AKI) in AML are limited. We report on the incidence of AKI in childhood AML and the risk factors associated with AKI episodes. METHODS: A retrospective cohort of 53 patients (≤18 years), with de novo AML, receiving chemotherapy over a 10-year period. All serum creatinine (SCr) levels during therapy-related hospitalizations were assessed to stage AKI episodes as per Kidney Disease: Improving Global Outcomes criteria. Severe AKI was defined as AKI stages 2 or 3 and urine output criteria were not used. AKI risk factors were assessed independently in both cycle 1 alone and combining all chemotherapy cycles. RESULTS: AKI developed in 34 patients (64%) with multiple AKI episodes in 10 patients (46 total episodes). Twenty-four severe AKI episodes occurred in 23 patients (43.4%) with a mean duration of 26.1 days (SD 7.3). In cycle 1, hyperleukocytosis was not predictive of AKI, but severe sepsis was an independent risk factor of severe AKI (odds ratio [OR]: 13.4; 95% CI 1.9-94.9). With cycles combined, all subjects with AKI had severe sepsis and older age (≥10 years) was associated with severe AKI (OR: 20.8; 95% CI 3.8-112.2). CONCLUSION: There was a high incidence of AKI in our AML cohort with a strong association with older age (≥10 years) and severe sepsis. Larger prospective studies are needed to confirm the high burden of AKI and risk factors in this susceptible population.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".