Identifying Factors Associated with Clinically Adjudicated Drug-Induced AKI in Children
Bibliographic record
Abstract
Background: Drug-induced acute kidney injury (DI-AKI) affects up to 33% of hospitalized children. Clinical adjudication of DI-AKI is challenging since AKI is multifactorial. We report clinical variables that influence ascertainment of DI-AKI cases and inter-rater reliability (IRR) of existing causality assessment tools (CAT) for adverse drug events. Methods: We analyzed data from the DIRECT study, an international, multi-center, observational cohort study of clinically adjudicated pediatric cases of AKI stage 2 associated with nephrotoxic medication (NTMx) exposure. Each case was adjudicated by two pediatric nephrologists using CAT. A third adjudicator acted as the tiebreaker. IRR was calculated using Krippendorff's alpha. We developed variables to capture exposure to NTMx and serum creatinine trends. We constructed a multivariable logistic regression model with clinically adjudicated DI-AKI as the outcome and clinical variables as predictors. Results: 115 (86.5%) out of 133 children were adjudicated as DI-AKI. The mean age was 12.2 ± 4.5 years, and the most frequent NTMx: vancomycin (43.6%), piperacillin/tazobactam (32.3%), and non-steroidal anti-inflammatory drugs (18.8%). AKI risk factors were comparable between clinically adjudicated DI-AKI and Not DI-AKI groups. Past medical history of malignancy, increased vascular capacity (i.e., sepsis or hypotension), and severe AKI treated with dialysis made DI-AKI adjudication less likely. Longer duration from the start of drug exposure to AKI onset made DI-AKI adjudication more likely. The IRR of the Liverpool (ka = 0.35) and the Naranjo (ka = 0.31) CAT were poor. Conclusions: DI-AKI adjudication is a complex and multifactorial process. Current CAT appear to be unreliable. Development of CAT specific to DI-AKI is needed to perform robust outcomes research. Funding: Other NIH Support - International Serious Adverse Events Consortium, National Library of Medicine (Grant #T15LM011271).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".