Comparing Measures of Cystatin C in Human Sera by Three Methods
Bibliographic record
Abstract
BACKGROUND: Cystatin C (Cys C) is measured by particle-enhanced nephelometric immunoassay (PENIA), particle-enhanced turbidimetric immunoassay (PETIA) and ELISA. AIM: To determine differences among these methods. METHOD: 80 normal human sera and 20 from patients with renal and/or heart disease were simultaneously assayed. Statistical analyses including receiver operating characteristics (ROC) of the three methods were compared. RESULTS: There was a highly significant correlation across the assay range between the ELISA and PENIA (r(2) = 0.94) and PETIA methods (r(2) = 0.95). Analysis of variance and bias were poor between the ELISA and the other two methods. Mean difference between ELISA and PETIA was 0.65 +/- 0.63 microg/ml, while it was 0.58 +/- 0.53 microg/ml between ELISA and PENIA. Accuracy (at 30% range) was 17 and 11% between ELISA and PETIA and ELISA and PENIA, respectively. Normalization of the ELISA by a factor of 0.66 improved this relationship. AUC of ROC curves of PENIA, ELISA and normalized ELISA to predict Cys C levels measured from PETIA were all above 0.87 (p = not significant between curves). Criterion values of ELISA*.66 method was close to PETIA measurements. CONCLUSION: There is a significant difference in measured human Cys C levels among the three methods, and normalization of ELISA narrows these differences.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".