Creatinine Assay Attainment of Analytical Performance Goals Following Implementation of IDMS Standardization
Bibliographic record
Abstract
BACKGROUND: The international initiative to standardize creatinine (Cr) assays by tracing reference materials to Isotope Dilution Mass Spectrometry (IDMS) assigned values was implemented to reduce interlaboratory variability and improve assay accuracy. OBJECTIVE: The aims of this study were to examine whether IDMS standardization has improved Cr assay accuracy (bias), interlaboratory variability (precision), total error (TE), and attainment of recommended analytical performance goals. METHODS: External Quality Assessment (EQA) data (n = 66 challenge vials) from Ontario, Canada, were analyzed. The bias, precision, TE, and the number of EQA challenge vials meeting performance goals were determined by assay manufacturer before (n = 32) and after (n = 34) IDMS implementation. RESULTS: = .5) with performance goals not consistently being met. Precision and TE goals based on biologic variation were attained by only 29% to 69% and 32% to 62% of challenge vials. CONCLUSIONS: While IDMS standardization has improved Cr assay accuracy and thus reduced TE, significant interlaboratory variability remains. Contemporary Cr assays do not currently meet the standards required to allow for accurate and consistent estimated glomerular filtration rate assessment and chronic kidney disease diagnosis across laboratories. Further improvements in Cr assay performance are needed.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".