Acute kidney injury in non-critically ill children treated with aminoglycoside antibiotics in a tertiary healthcare centre: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Aminoglycosides (AG) cause acute kidney injury (AKI), but the incidence and severity distribution are unclear, particularly in non-critically ill children. We determined the incidence, severity and risk factors of AG-associated AKI and assessed for associations with longer hospitalization and higher costs. METHODS: At Texas Children's Hospital, we conducted a retrospective cohort study of children treated with AG for ≥ 5 days in 2005, excluding children with admission primary renal diagnoses. AKI was defined by the paediatric Risk, Injury, Failure, Loss, End Stage Kidney Disease (pRIFLE) and Acute Kidney Injury Network (AKIN) definitions. Multiple logistic and linear regression analyses were used to assess independence of associations with outcomes. RESULTS: Five hundred and fifty-seven children [mean ± SD age = 8.0 ± 5.9 years, 286 (51%) male, 489 (88%) gentamicin] were studied. The AKI rate was 33% and 20% by pRIFLE and AKIN definitions, respectively. Longer treatment, higher baseline estimated glomerular filtration rate, being on a medicine (versus surgical) treatment service and prior AG treatment were independent risk factors for AKI development. AKI by pRIFLE or AKIN was independently associated with longer hospital stay and higher total hospital costs. The pRIFLE definition was more sensitive for AKI detection, but the AKIN definition was more strongly related to outcomes. CONCLUSIONS: AKI is common and associated with poorer outcomes in non-critically ill children treated with AG. Future research should attempt to understand how to best define AKI in the non-critical illness paediatric setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".