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Record W4414741036 · doi:10.1093/clinchem/hvaf086.117

A-121 Analytical performance evaluation of the next generation Abbott enzyme assays on the Alinity c system

2025· article· en· W4414741036 on OpenAlexaffabout
Meshach Asare-Werehene, Pow Lee Cheng, Xiaoyan Wang, Marvin Berman, Vathany Kulasingam

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFungal Plant Pathogen Control
Canadian institutionsUniversity Health NetworkToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsAlanine aminotransferaseAlkaline phosphataseAspartate AminotransferasesLinearityEnzymeExternal quality assessmentMeasurement uncertaintyAccreditation

Abstract

fetched live from OpenAlex

Abstract Background Liver enzyme assays, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyltransferase (GGT), and alkaline phosphatase (ALP), are essential in clinical laboratories for assessing liver function, diagnosing liver diseases, and monitoring treatment efficacy. Thus, accurate measurement of these enzyme activities is crucial for clinical diagnostics. Recently, Abbott introduced a next generation of Alinity and Architect enzyme assays, incorporating a multianalyte calibrator (Consolidated Chemistry Calibrator, ConCC) with extended stability. For these new formulation assays, both ConCC and calibration factors are offered for all the assays except GGT, which had better performance with the ConCC calibrator. These assays achieve high Sigma metrics on the Alinity c system, minimizing variability and errors. This study evaluated the analytical performance of these newly developed next generation assays on the Abbott Alinity c platform. Methods Using 2 levels of quality control (QC) material (Bio-Rad Chemistry UA) and 3 pooled patient samples, we assessed imprecision by measuring these 5 samples twice per day (morning and afternoon), for five days. Acceptable imprecision and bias were determined based on the Accreditation Canada Diagnostics (ACDx) recommendations. Linearity testing consisted of 6 levels of commercially available linearity materials, with 3 replicates per level. Method comparison between the next generation assays and on-market conventional assays were evaluated in duplicates using patient specimens (n= 145 – 155) on the Abbott Alinity c platform. Passing-Bablok and Bland-Altman plots were used for the method comparison analyses. The precision and bias (external standard) studies were used to calculate the Sigma-metric using the formula, Sigma-metric=(%TEa-|%bias|)/%CV. TEa was defined based on ACDx and Clinical Laboratory Improvement Amendments (CLIA) recommendations. Results The Alinity next generation assays demonstrated acceptable imprecision, meeting the ACDx goals of ±2 U/L for concentrations =40 U/L and ±4% for concentrations >40 U/L for AST, ALT and GGT, and for ALP at ±4 U/L for concentrations =100 U/L and ±4% for concentrations >100 U/L. Furthermore, these assays exhibited linearity across the six concentration levels tested. All next generation Alinity clinical chemistry enzyme assays showed a Pearson*s R value of 1.0, indicating a strong linear correlation. The linearity slope ranged from 0.76 (ALP2) to 1.04 (AST2) whereas the y-intercept ranged from –59.40 (GGT2) to 8.78 (AST2). The majority of next generation assays performed at or above 6 Sigma. Good agreements were observed between the on-market assay and the next generation (ConCC and factor calibrated) assays. Conclusion The Alinity next generation assays (ALT2, AST2, GGT2 and ALP2) demonstrated acceptable performance for precision and linearity, with good agreement with the conventional factor-based assays on the Alinity c system. These next generation assays had a high sigma value; hence laboratories can expect excellent performance. The precision and method comparison agreement with the conventional assays were satisfactory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.175
GPT teacher head0.320
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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