Unique Approach to Derivation of Random Error in Laboratory Assays: Application to Glycohemoglobin Testing Demonstrates Poor Clinical Performance for Immunochemistry Assay
Bibliographic record
Abstract
The measurement of glycohemoglobin is the best measure of mean glucose within a 3-4 month range. As it is used for patient education, counseling, feedback control, and ultimately for patient motivation, its measurement should be optimally accurate and precise. Duplicate hemoglobin A1c readings were used to determine physiological (changes over time between measurements) and analytic variation of two widely used laboratory assays: Bio-Rad Variant II's high-performance liquid chromatography (HPLC) system and Roche's immunoassay. The average variation of grouped duplicates was calculated and graphed against corresponding time intervals. Regression to the y-intercept (0 day separation between readings) was used to determine the analytic variation. Analytic coefficients of variation (CVs) for the HPLC and immunoassay were determined as 2.6% and 5.1%, respectively. The CV of the immunoassay method exceeds physiologically established limits of 2-3% and those of the National Glycohemoglobin Standardization Program (3-4%). The Bio-Rad HPLC system produces a CV within these limits.
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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 | 0.054 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".