Estimated blood loss to urine output ratio during partial nephrectomy as a predictor of postoperative acute kidney injury in a hereditary renal cancer-enriched population
Bibliographic record
Abstract
INTRODUCTION: We aimed to assess whether the intraoperative estimated blood loss (EBL) to urine output (UOP) ratio (EBL/UOP) is a predictor of postoperative acute kidney injury (AKI) in a cohort of patients enriched with hereditary renal cancer syndromes undergoing partial nephrectomy (PN). METHODS: We performed a retrospective chart review of patients who underwent PN at our institution from January 2006 to October 2024. We recorded and analyzed the clinical, demographic, and intraoperative characteristics of all patients. RESULTS: A total of 1166 PNs (761 patients and 5903 renal tumors) were analyzed, of which 484 (41.5%) developed postoperative AKI. The average EBL/UOP was 1.06 (0.46-2.35) for patients without AKI and increased as AKI worsened, with a ratio of 5.00 (2.34-9.43) in patients with KDIGO AKI grade 3 (p<0.001). EBL/UOP was associated with AKI in all patients (odds ratio [OR] 1.079, p=0.002) and those with bilateral native kidneys (OR 1.083, p=0.003). After adjustment in patients with solitary kidney, no AKI association with EBL/UOP (OR 1.039, p=0.447) was found. CONCLUSIONS: EBL/UOP is a novel tool associated with the increased risk of developing post-PN AKI in select patients. In multiplex and repeat PNs, a higher ratio can assist the surgical team in identifying patients at risk of developing AKI. Prospective evaluation involving management strategies based on the EBL/UOP is needed to determine its true utility in clinical practice and generalization in the broader PN population.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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 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".