Bibliographic record
Abstract
PURPOSE OF REVIEW: This review will summarize and discuss the role of cystatin C in the diagnosis of acute kidney injury. RECENT FINDINGS: Cystatin C is easily measured and has the characteristics of an ideal marker of kidney function. Data suggest that cystatin C is modified by age, sex, muscle mass, obesity, smoking status, thyroid function, inflammation, and malignancy. These factors suggest the need for age-specific and sex-specific reference standards. Cystatin C-based glomerular filtration rate estimates may perform better than creatinine in selected patient populations (elderly, children, transplantation, cirrhosis, malnourished). Cystatin C has been evaluated for the early diagnosis of acute kidney injury (AKI) in several populations. Serum cystatin C has value for the diagnosis of acute kidney injury; however, it has often performed similarly to creatinine. Urinary cystatin C has potential as an early marker. SUMMARY: Cystatin C is an accurate biomarker for the early detection of AKI, and may, in selected populations, be superior to creatinine; however, data have been inconsistent. It also has reasonable discrimination for important outcomes such as death and renal replacement therapy (RRT). Additional studies are needed that focus on the cost-effectiveness of earlier detection of AKI with cystatin C compared with creatinine, and whether these biomarkers have complementary value.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".