Cystatin C predicts renal function impairment after partial or radical tumor nephrectomy
Bibliographic record
Abstract
Abstract Purpose To test the value of preoperative and postoperative cystatin C (CysC) as a predictor on kidney function after partial (PN) or radical nephrectomy (RN) in renal cell carcinoma (RCC) patients with normal preoperative renal function. Methods From 01/2011 to 12/2014, 195 consecutive RCC patients with a preoperative estimated glomerular filtration rate (eGFR) > 60 ml/min/1.73m2 underwent surgical RCC treatment with either PN or RN. Logistic and linear regression models tested for the effect of CysC as a predictor of new-onset chronic kidney disease in follow-up (eGFR < 60 ml/min/1.73m2). Moreover, postoperative CysC and creatinine values were compared for kidney function estimation. Results Of 195 patients, 129 (66.2%) underwent PN. In postoperative and in follow-up setting (median 14 months, IQR 10–20), rates of eGFR < 60 ml/min/1.73m2 were 55.9 and 30.2%. In multivariable logistic regression models, preoperative CysC [odds ratio (OR): 18.3] and RN (OR: 13.5) were independent predictors for a reduced eGFR < 60 ml/min/1.73m2 in follow-up (both p < 0.01), while creatinine was not. In multivariable linear regression models, a difference of the preoperative CysC level of 0.1 mg/dl estimated an eGFR decline in follow-up of about 5.8 ml/min/1.73m2. Finally, we observed a plateau of postoperative creatinine values in the range of 1.2–1.3 mg/dl, when graphically depicted vs. postoperative CysC values (‘creatinine blind area’). Conclusion Preoperative CysC predicts renal function impairment following RCC surgery. Furthermore, CysC might be superior to creatinine for renal function monitoring in the early postoperative setting.
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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 | 0.000 | 0.000 |
| 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".