The Impact of Interlaboratory Differences in Cystatin C Assay Measurement on Glomerular Filtration Rate Estimation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cystatin C (CysC) is a promising marker of GFR. Several equations have been derived to estimate GFR from its serum concentration. Heterogeneity in the performance of these equations exists in validation studies even when the same CysC assay from the same manufacturer is utilized. This study was designed to examine the differences in CysC and GFR estimation (eGFR) using Siemens' nephelometric immunoassay and the Mayo Clinic equation. The ability of the eGFRs to predict measured GFR was also examined. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Ninety-seven split samples were sent to laboratories at Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada, and at the Mayo Clinic in Rochester, Minnesota. RESULTS: The mean CHEO CysC was 0.17 mg/L (10%) lower than the mean Mayo Clinic CysC. Using the Mayo Clinic equation, the mean eGFR difference was 7.2 ml/min per 1.73 m(2) (15%). Approximately 36% of the results agreed within 10%, while 13% were discordant by greater than 30%. Larger absolute differences in mean eGFR between the two laboratories were found in the subgroup with CysC less than 1.41 mg/L as compared with the subgroup greater than 1.41 mg/L (9.5 versus 5.0 ml/min per 1.73 m(2)). Correction of CHEO values to the Mayo Clinic did not improve GFR estimation. CONCLUSIONS: Significant differences in CysC measurement exist between laboratories using the same assay by the same manufacturer and these lead to clinically relevant differences in GFR estimation. This interlaboratory variability needs to be recognized when interpreting and comparing CysC and eGFR results.
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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 | 0.091 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".