Baseline Serum Creatinine (BSCr) Estimation Methods in Hospitalized Children
Bibliographic record
Abstract
Acute kidney injury (AKI) in hospitalized children is common and associated with poor outcomes. According to international guidelines, baseline serum creatinine (BSCr) or the lowest SCr in the 3 to 6 months before hospital admission, is required to define AKI. BSCr is not available in most children and must be estimated. For the last 20 years, the traditional method to estimate BSCr has been to assume normal kidney function and use either the Chronic Kidney Disease in Children (CKiD) or the Hoste glomerular filtration rate equations (eGFR) to back-calculate estimated BSCr. However, the accuracy of this method is variable, and they rely on tenuous assumptions. Three new non-eGFR-based BSCr estimation methods (the AKI Baseline Creatinine [ABC] equations: ABC (advanced), ABC (-cr) and ABC (simple) were recently developed from a single-centre cohort but have not been validated in Canadian children. I performed a retrospective cohort study of children admitted to SickKids hospital from 2021 to 2022 and found that ABC (advanced) and ABC (-cr) estimated BSCr with good correlation (R2: 0.75 and R2: 0.74, respectively) and demonstrated the highest accuracy of all methods evaluated (approximately 65% of estimated BSCr values within 30% of the measured BSCr). The ABC (simple) was no better than the traditional Hoste method for estimating BSCr (not shown). When AKI was ascertained using each BSCr method, all methods had >80% agreement with measured BSCr-defined AKI. The new ABC (advanced) and ABC (-cr) methods most accurately estimated measured BSCr in hospitalized children. Improved accuracy in estimating BSCr may significantly improve AKI detection and facilitate accurate AKI intervention, but they may not be feasible to apply to all patients. More research is needed to evaluate BSCr estimation methods in different patient populations and on integrating BSCr estimation equations in electronic medical records to identify pediatric AKI.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.001 | 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".