B-098 Analytical Performance Evaluation of Sigma Strong Clinical Chemistry Assays on the Alinity c System
Bibliographic record
Abstract
Abstract Background The aim of this study was to evaluate the analytical performance of 5 Abbott next generation (Sigma Strong) clinical chemistry assays on the Abbott Alinity c system. Method comparison, precision, linearity, accuracy, and sigma metrics were assessed for Albumin BCG2 (Bromocresol green), Albumin BCP2 (Bromocresol purple), Cholesterol2, Total Protein2 and Amylase2. Methods Method comparison between new (second generation) and current on market Alinity assays was performed by measuring 126–138 serum/plasma samples in duplicate. Assessment of imprecision was performed by running 2 levels of quality control material (BioRad; Chemistry UA) and 3 pooled patient samples 5x twice per day for 5 days. 10 replicates of ERM-DA470 k IFCC material for albumin, ERM-AD456 k IFCC material for amylase, NIST SRM 927 material for total protein, and NIST 1951cL1 for cholesterol were run to determine the accuracy and calculated sigma value for each analyte. Linearity testing was performed by running 5–6 levels of commercially available linearity materials in replicates of 3. Bias, acceptable imprecision, and total allowable error were based on Clinical Laboratory Improvement Amendments and Accreditation Canada Diagnostics guidelines. Statistical analysis was performed using EP evaluator. Results Data for method comparison (Passing Bablok), precision (Range of total %CV across 5 concentrations), accuracy (% Bias from target value), and linearity (Range of % recovery) for 5 Abbott second generation clinical chemistry assays are shown in Table 1. All assays demonstrated ≥6 Sigma performance. Conclusion The Abbott second generation (Sigma Strong) clinical chemistry assays on the Alinity c system showed acceptable performance for precision, accuracy, linearity, and agreement with the on-market Alinity c clinical chemistry assays.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.005 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".