Sample matrix and high-sensitivity cardiac troponin I assays
Bibliographic record
Abstract
Background Manufacturers of high-sensitivity cardiac troponin (hs-cTn) assays have restricted use of what sample types or matrices are acceptable to use for measurement. Our goal was to evaluate the comparability of the Siemens ADVIA Centaur hs-cTnI assay across different matrices and under different storage conditions. Methods Three different QC-plasma matrices were evaluated for imprecision <10 ng/L. Passing-Bablok regression and difference plots were determined for cTnI concentrations spanning the reference interval (limit of quantification to male 99th-percentile: 2.5 ng/L to <60 ng/L) between serum and lithium heparin plasma, lithium heparin and EDTA plasma and between the Siemens and Abbott hs-cTnI assays. Stability at room temperature (RT) and 2-8 °C was also assessed across the three matrices. Results Over 16-weeks the SDs were ≤1.0 ng/L for QCs ranging from 5.0 to 8.3 ng/L. Across the reference interval there was excellent agreement between lithium heparin plasma and serum for the Siemens hs-cTnI assay (slope=0.98/intercept=-0.1), however, cTnI concentrations were proportionally lower in EDTA as compared to lithium heparin plasma (slope=0.90, 95% CI: 0.88-0.92). In lithium heparin plasma the Siemens hs-cTnI concentrations were higher than the Abbott hs-cTnI concentrations (slope=1.26/intercept=-0.2). Stability of cTnI in lithium heparin plasma as compared in serum and EDTA plasma appeared more labile, with decreases ≥20% in concentrations evident as early as 1-day in storage at RT. Conclusions There is excellent agreement in concentrations between lithium heparin plasma and serum with the Siemens ADVIA Centaur hs-cTnI assay; however, cTnI concentrations in EDTA plasma are lower. Reference intervals and clinical studies in EDTA plasma for the Centaur hs-cTnI assay are required before clinical use.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".