Epidemiology of acute kidney injury in children: a report from the 26th Acute Disease Quality Initiative (ADQI) consensus conference
Bibliographic record
Abstract
The nephrology and critical care communities have seen an increase in studies exploring acute kidney injury (AKI) epidemiology in children. As a result, we now know that AKI is highly prevalent in critically ill neonates, children, and young adults. Furthermore, children who develop AKI experience greater morbidity and higher mortality. Yet knowledge gaps still exist that suggest a more comprehensive understanding of AKI will form the foundation for future efforts designed to improve outcomes. In particular, the areas of community acquired AKI, AKI in non-critically ill children, and cohorts from low-middle income countries have not been well studied. Longer-term functional outcomes and patient-centric metrics including social determinants of health, quality of life, and healthcare utilization should be the foci of the next phase of scholarship. Current definitions identify AKI-based upon evidence of dysfunction which serves as a proxy for injury; biomarkers capable of identifying injury as it occurs are likely to more accurately define populations with AKI. Despite the strength of the association, the causal and mechanistic relationships between AKI and poorer outcomes remain inadequately examined. A more robust understanding of the relationship represents a potential to identify therapeutic targets. Once established, a more comprehensive understanding of AKI epidemiology in children will allow investigation of preventive, therapeutic, and quality improvement interventions more effectively.
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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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".