Plasma Cystatin C and Acute Kidney Injury after Cardiopulmonary Bypass
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Little is known about the performance of plasma cystatin C (CysC) in patients undergoing cardiopulmonary bypass (CPB) and its utility in the early diagnosis of acute kidney injury (AKI). In this post hoc analysis, the goal was to determine whether plasma cystatin C, measured 2 hours after the conclusion of CPB, is a reliable marker of AKI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Plasma CysC was measured in 150 patients undergoing CPB at the following times: preoperatively, 2 hours after the conclusion of CPB, postoperative day 1, and postoperative day 2. Plasma CysC levels were related to the development of AKI as defined by an increase in serum creatinine of >or=50% or >or=0.3 mg/dl from baseline up to 3 days postoperative. Mixed linear models were used to evaluate the relationship of serial plasma CysC values with AKI. The discriminatory capacity of plasma CysC was estimated using receiver operating characteristic curves. Logistic regression was utilized to assess the adjusted relationship between plasma CysC and subsequent AKI. RESULTS: AKI developed in 47 (31.3%) patients. Plasma CysC was higher at all times among patients who developed AKI compared with those who did not (P < 0.0001). The discriminatory capacity of plasma CysC measured preoperatively and 2 hours after the conclusion of CPB was modest. CONCLUSIONS: Serial measures of plasma CysC are highly correlated with the development of AKI. However, the discriminatory capacity of plasma CysC as an early marker of AKI remains limited.
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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 | 0.004 |
| 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 | 0.001 |
| 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".