Cystatin C is a biomarker for predicting acute kidney injury in patients with acute-on-chronic liver failure
Bibliographic record
Abstract
AIM: To investigate serum cystatin C level as an early biomarker for predicting acute kidney injury (AKI) in patients with acute-on-chronic liver failure (ACLF). METHODS: Fifty-six consecutive patients with hepatitis B virus-related ACLF who had normal serum creatinine (Cr) level (< 1.2 mg/dL in men, or < 1.1 mg/dL in women) were enrolled in the Liver Failure Treatment and Research Center of Beijing 302 Hospital between August 2011 and October 2012. Thirty patients with chronic hepatitis B (CHB) and 30 healthy controls in the same study period were also included. Measurement of serum cystatin C (CysC) was performed by a particle-enhanced immunonephelometry assay using the BN Prospec nephelometer system. The ACLF patients were followed during their hospitalization period. RESULTS: In the ACLF group, serum level of CysC was 1.1 ± 0.4 mg/L, which was significantly higher (P < 0.01) than those in the healthy controls (0.6 ± 0.3 mg/L) and CHB patients (0.7 ± 0.2 mg/L). During the hospitalization period, eight ACLF patients developed AKI. Logistic regression analysis indicated that CysC level was an independent risk factor for AKI development (odds ratio = 1.8; 95%CI: 1.4-2.3, P = 0.021). The cutoff value of serum CysC for prediction of AKI in ACLF patients was 1.21 mg/L. The baseline CysC-based estimated glomerular filtration rate (eGFR(CysC)) was significantly lower than the creatinine-based eGFR (eGFR(CG) and eGFR(MDRD)) in ACLF patients with AKI, suggesting that baseline eGFR(CysC) represented early renal function in ACLF patients while the Cr levels were still within the normal ranges. CONCLUSION: Serum CysC provides early prediction of renal dysfunction in ACLF patients with a normal serum Cr level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".