The usefulness of cystatin C and related formulae in pediatrics
Bibliographic record
Abstract
Serum creatinine does not share the properties of an ideal marker of glomerular filtration rate (GFR) like inulin, but continues to be the most widely used endogenous marker of GFR. In the search of a better biomarker of GFR, the small molecular weight protein cystatin C has been introduced with features more similar to that of inulin, such as constant production and no non-renal elimination. However,it has not enjoyed widespread use despite its significantly improved diagnostic performance in the detection of impaired GFR and its independence of body composition. A variety of formulae based on either cystatin C or creatinine or both have been developed to estimate GFR. We summarize the currently used methods of GFR measurement, their limitations and analytical errors. The review also summarizes the history, features and the feasibility of cystatin C measurements as well as the most widely used formulae for the estimation of GFR in children. The diagnostic performance of the cystatin C derived eGFR formulae at various levels of GFR is also discussed. An eGFR formula derived from pooled studies analyzing both creatinine and cystatin C, and using a biology-based mathematical approach maybe advantageous.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".