Serum and urinary biomarkers for predicting acute kidney injury after partial nephrectomy
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the ability of specific biomarkers to predict acute kidney injury (AKI) after partial nephrectomy. METHODS: A prospective study of 89 patients undergoing partial nephrectomy was conducted in the First Affiliated Hospital of Fujian Medical University. The patients were divided into two groups according to AKI status: an AKI group and non-AKI group. Receiver operator characteristic (ROC) curves were generated and the areas under the curve (AUCs) were compared. RESULTS: Twenty-eight subjects (31.5%) developed AKI while sixty-one subjects (68.5%) did not. Vascular clamping time in the AKI group was longer than that in the non-AKI group (29 ± 17 min vs. 24 ± 9 min, P = 0.042). Eight patients (28.6%) received blood infusion in the AKI group compared with five patients (8.2%) in the non-AKI group (P = 0.021). The area under ROC curve for AKI prediction was 0.792 [95% confidence interval (CI) 0.697 to 0.888, P < 0.000] for serum cystatin C 24 hours after surgery and 0.756 (95% CI 0.656 to 0.857, P < 0.000) for serum cystatin C 48 hours after surgery. Multivariate regression analysis showed transfusion [Hazard ratio (HR) 3.712, P = 0.044] and 24 hours serum cystatin C (HR 41.594, P = 0.001) correlated with AKI. CONCLUSIONS: Postoperative serum cystatin C may be an early predictor for AKI after partial nephrectomy. Transfusion may be an independent risk factor for AKI after partial nephrectomy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".