A novel Sigma metric encompasses global multi-site performance of 18 assays on the Abbott Alinity system
Bibliographic record
Abstract
OBJECTIVES: The Abbott Alinity family of chemistry and immunoassay systems recently launched with early adopters contributing imprecision and bias data, which was consolidated to assess the performance of Alinity assays across multiple sites using the Sigma metric. Multi-site Sigma metrics were determined for 3 ion-selective electrodes, 12 photometric assays, and 3 immunoassays across 11 independent laboratory sites in 9 countries. METHODS: Total allowable error (TEa) goals followed a previously defined hierarchy that used CLIA as the primary goal. Bias was calculated against the Abbott ARCHITECT system using Passing-Bablok regression analysis using individual site data or pooled aggregate data. Sigma metrics were calculated as (%TEa - |% bias|)/%CV. For individual-site analysis, the Sigma metrics for each assay were compared using the individual-site and the pooled biases. For multi-site analysis, the average CV and the pooled bias were used to generate a Pooled Sigma metric encompassing the global performance for a given assay. RESULTS: A total of 97 individual-site and 18 Pooled Sigma metrics were calculated for available assays. Individual Sigma metrics varied across sites, with 90% of assays performing 4 Sigma or higher, and 17 of 18 Pooled Sigma metrics indicated performance greater than 4 Sigma. Sigma metrics were significantly improved in 16 assays when using pooled bias rather than individual-site bias. CONCLUSIONS: This multi-center study applies a novel application of Sigma metrics to the first Alinity users and reveals analytical performance of greater than 4 Sigma for vast majority of assays. Laboratories with limited resources can leverage larger data sets for Pooled Sigma metric analysis, providing a tool to assess the consistency of analytical performance from multiple sites.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".