Assessing matrix, interferences and comparability between the Abbott Diagnostics and the Beckman Coulter high-sensitivity cardiac troponin I assays
Bibliographic record
Abstract
BACKGROUND: Analytical evaluation of high-sensitivity cardiac troponin (hs-cTn) assays, with particular attention to imprecision, interferences and matrix effects, at normal cTn concentrations, is of utmost importance as many different clinical algorithms use concentration cutoffs <10 ng/L for decision-making. The objective for the present analytical study was to compare the new Beckman Coulter hs-cTnI assay (Access hsTnI) to Abbott's hs-cTnI assay in different matrices and for different interferences, with a focus on concentrations <10 ng/L. METHODS: The limit of blank (LoB) and the limit of detection (LoD) were determined in different matrices for the Beckman hs-cTnI assay. Passing-Bablok regression and difference plots were determined for 200 matched lithium heparin and EDTA plasma samples for the Beckman assay and 200 lithium heparin samples for the Abbott assay. Both EDTA and heparin plasma samples were also evaluated for stability under refrigerated conditions, for endogenous alkaline phosphatase interference and for hemolysis and icterus. RESULTS: The Beckman hs-cTnI assay LoB was 0.5 ng/L with the following range of LoDs=0.8-1.2 ng/L, with EDTA plasma yielding lower concentrations as compared to lithium heparin plasma (mean difference=-14.9%; 95% CI=-16.9 to 12.9). Below 10 ng/L, lithium heparin cTnI results from the Beckman assay were on average 1.1 ng/L (95% CI=0.7 to 1.5) higher than the Abbott results, with no difference between the methods when using EDTA plasma (mean difference =-0.1 ng/L; 95% CI=-0.3 to 0.2). Low cTnI concentrations were less effected by interferences in EDTA plasma. CONCLUSIONS: The Access hsTnI method can reliably detect normal cTnI concentrations with both lithium heparin and EDTA plasma being suitable matrices.
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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 | 0.034 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".