Cystatin C for early detection of acute kidney injury after laparoscopic partial nephrectomy
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Mortality due to AKI has not changed significantly over the past 50 years. This is due in part to failure to detect early AKI and to initiate appropriate therapeutic measures. There is therefore a need to identify biomarkers that would improve the early detection of AKI. The objective of this study was to assess whether cystatin C levels obtained at specific timepoints during laparoscopic partial nephrectomy (PN) could be early predictors of AKI. MATERIALS AND METHODS: Twenty-five patients underwent laparoscopic PN for organ-confined tumors. All procedures were performed by two surgeons in a single institution. Plasma samples were collected preoperatively, and post-unclamping at 5, 20, 120 min and on the day following surgery. Plasma cystatin C was measured by enzyme-linked immunosorbent assay. Correlation between levels of cystatin C and other parameters of interest were assessed in order to define cystatin C ability to predict AKI and loss of renal function following laparoscopic PN. RESULTS: The mean baseline eGFR was 93 ml/min/1.73 m(2). Warm ischemia time varied between 16 and 44 min. Post-operative day 1 (POD1) cystatin C levels compared to baseline were increased in 13 (52%) of the patients. There was a high correlation between the difference of POD 1 and baseline value, and eGFR in the immediate postoperative period (r = -0.681; P = 0.0002) and at 12-month follow-up (r = -0.460, P = 0.048). However, the variation in cystatin C levels at earlier timepoints were not associated to AKI nor renal function. CONCLUSIONS: High increase in POD 1 cystatin C levels from baseline may help identify patients with AKI and those at higher risk of chronic kidney disease, following laparoscopic PN.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".