Diagnostic performance of serum blood urea nitrogen to creatinine ratio for distinguishing prerenal from intrinsic acute kidney injury in the emergency department
Bibliographic record
Abstract
BACKGROUND: The blood urea nitrogen to creatinine ratio (BCR) has been used since the early 1940s to help clinicians differentiate between prerenal acute kidney injury (PR AKI) and intrinsic AKI (I AKI). This ratio is simple to use and often put forward as a reliable diagnostic tool even though little scientific evidence supports this. The aim of this study was to determine whether BCR is a reliable tool for distinguishing PR AKI from I AKI. METHODS: We conducted a retrospective observational study over a 13 months period, in the Emergency Department (ED) of Nantes University Hospital. Eligible for inclusion were all adult patients consecutively admitted to the ED with a creatinine >133 μmol/L (1.5 mg/dL). RESULTS: Sixty thousand one hundred sixty patients were consecutively admitted to the ED. 2756 patients had plasma creatinine levels in excess of 133 μmol/L, 1653 were excluded, leaving 1103 patients for definitive inclusion. Mean age was 75.7 ± 14.8 years old, 498 (45%) patients had PR AKI and 605 (55%) I AKI. BCR was 90.55 ± 39.32 and 91.29 ± 39.79 in PR AKI and I AKI groups respectively. There was no statistical difference between mean BCR of the PR AKI and I AKI groups, p = 0.758. The area under the ROC curve was 0.5 indicating that BCR had no capacity to discriminate between PR AKI and I AKI. CONCLUSIONS: Our study is the largest to investigate the diagnostic performance of BCR. BCR is not a reliable parameter for distinguishing prerenal AKI from intrinsic 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".