Acute Kidney Injury Diagnostic Accuracy and Implications of Different Baseline Creatinine Equations
Bibliographic record
Abstract
Introduction Acute kidney injury (AKI) definitions rely on known baseline creatinine (Cr b ), unavailable in up to 75% of hospitalized children. New equations (ABC methods) for estimating Cr b were derived from children without kidney disease. We aim to externally validate ABC methods in an international cohort and assess how different Cr b equations alter AKI epidemiology. Methods AWARE was a prospective international study of critically ill children (age 0-25 years) from 32 PICUs. A subset of AWARE (n=2451) with known Cr b (gold standard) is used to validate ABC methods using statistical measures of precision (R 2 ) and accuracy (within 10% or 30% of gold standard). The entire cohort (n=4984) is used to determine how different Cr b estimating equations (3 ABC equations and 4 published eGFR equations imputing Cr b ) impact AKI incidence and its association with key clinical outcomes, including 28-day mortality, using univariate and multivariate analysis. Results The ABC-Age equation (requiring only age) demonstrated similar accuracy and precision compared to existing Cr b equations. The ABC-Creatinine equation (includes age and hospital creatinine value) outperformed existing Cr b equations by up to 15% in precision (ABC-Creatinine R 2 =0.51 versus FAS R 2 =0.36) and 32% in accuracy (ABC-Creatinine 66% versus Original Schwartz 34%). For the entire cohort, AKI incidence varied from 7-12% depending on Cr b definition. ABC equations are associated with clinical outcomes similarly to existing Cr b equations. Conclusion ABC-Creatinine equation (with minimal variables) outperformed existing Cr b equations for accuracy and precision as the optimal method for Cr b estimation. Cr b definition variability alters AKI incidence and epidemiology, necessitating standardization.
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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 | 0.045 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".