Cystatin C and acute changes in glomerular filtration rate
Bibliographic record
Abstract
The identification of an effective marker of acutely changing kidney function is a priority in clinical nephrology. While serum creatinine is the most widely used surrogate for glomerular filtration rate (GFR), its vulnerability to non-glomerular clearance results in biased estimates of GFR and may delay the identification of acute changes. Alternatively, cystatin C (CysC) has been recognized as a promising marker of GFR. Controlled physiological studies in diabetes, protein-induced glomerular hyperfiltration and extreme exercise demonstrated that acute changes in CysC provide a better approximation of GFR than serum creatinine. Clinical studies examining contrast induced nephropathy, acute kidney injury, and kidney transplantation have also demonstrated several possible advantages of CysC with respect to accurately measuring GFR and early diagnosis of renal dysfunction. CysC measurements also provide ancillary benefits such as improved prediction of patient outcomes and prognosis. Our aim was to review the literature on short-term changes in CysC over days, weeks and months to explore the clinical utility of CysC in the acute setting. Based on existing evidence, CysC may improve clinicians' ability to detect acute changes in kidney function.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".