Cystatin C as a biomarker for estimating glomerular filtration rate
Bibliographic record
Abstract
PURPOSE OF REVIEW: Glomerular filtration rate (GFR) is rarely measured in clinical practice because of the complexity of the measurement. As such, kidney function is typically estimated using validated study equations, which use readily available data including age, sex, race, and serum creatinine as filtration marker. Contemporary research suggests that cystatin C may be an improved alternative to creatinine for inclusion in GFR estimating equations. The purpose of this article is to evaluate the benefits and limitations of using cystatin C as a biomarker of filtration. RECENT FINDINGS: Cystatin C has fewer non-GFR determinants, when compared with serum creatinine. Use of serum cystatin C avoids the limitations related to both diet and muscle mass that affect serum creatinine. Cystatin C may be more accurate than serum creatinine in estimating GFR, and is more strongly associated with all-cause mortality and cardiovascular events. SUMMARY: Cystatin C has some advantages over serum creatinine in estimating GFR. The use of cystatin C as a confirmatory biomarker in deciding medication dosages or as a confirmatory test in patients with an uncertain diagnosis of chronic kidney disease may be beneficial.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".