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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".