Analytical evaluation and Sigma metrics of 6 next generation chemistry assays on the Abbott Architect system
Bibliographic record
Abstract
BACKGROUND: We evaluated analytical and Sigma performance for 6 next generation chemistry assays on the Abbott Architect c8000 system. METHODS: Albumin with bromocresol purple or green, amylase, cholesterol, total protein, and urea nitrogen were analyzed using photometric technology. Analytical performance goals were defined based on Accreditation Canada Diagnostics (ACD) and Clinical Laboratory Improvement Amendments (CLIA). Precision study consisted of testing 2 quality control concentrations and 3 patient serum sample pools, twice a day in quintuplicate over 5 days. Linearity testing consisted of 5-6 concentrations of commercial linearity materials. We tested a minimum of 120 serum/plasma specimens on the new and current Architect methods for comparison. We assessed accuracy with reference materials for 5 assays, and a calibration standard for cholesterol. Bias from the reference standard target value was used for Sigma metric analysis. RESULTS: Observed total imprecision of the assays ranged from 0.5 to 4%, meeting pre-defined goals. Linearity was acceptable over the tested range. Measurements on the new and current Architect methods were comparable. Accuracy ranged from 0 to 2.0% absolute mean difference from target value. All 6 next generation clinical chemistry assays demonstrated Six Sigma quality, using CLIA standards. CONCLUSION: Applying ACD recommendations, 5 assays showed Six Sigma, while cholesterol showed Five Sigma performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".