Validation of Analytical Performance Limits for Accuracy with High-Sensitivity Cardiac Troponin Assays
Bibliographic record
Abstract
Fifteen years ago, a publication on a prototype high-sensitivity cardiac troponin (hs-cTn) I assay determined a CV of 10.9% at a mean concentration of 10.2 ng/L using quality control material (SD = 1.11 ng/L) (1). Acceptable analytical variation used in in this publication to assess early diagnostic utility (i.e., change or the delta for serial concentrations) of the hs-cTn assay was ±3.33 ng/L (1). In recent years, significant attention has been focused on defining what constitutes a clinically significant change or difference in hs-cTn concentrations, while there has been less published data and fewer studies on the analytical limits for the accuracy of hs-cTn assays (2). There has been some interest in biological variation as a quality metric for hs-cTn assays, with a meta-analysis indicating similar reference change values in healthy individuals over the short term with hs-cTnI (−25.6 to 34.4%) and hs-cTnT (−26.2 to 35.6%) (3). However, higher biological variation than these estimates has been reported, with clinical simulation data providing different estimates for acceptable analytical variation in relation to patient classification for myocardial infarction (4). A recent year-long multisite study assessing analytical variation for hs-cTn determined that acceptable analytical variation below 10 ng/L was ±3 ng/L with 30% variation acceptable above and near the 99th-percentile concentrations (5). Thirty-six sites participated in this study with patient material (3 samples: normal, female 99th percentile, male 99th percentile) tested monthly, yielding 2142 results from 6 different hs-cTn assays over the year (5). We aimed to validate these analytical limits for accuracy by reassessing external quality assurance data from 2 large proficiency testing programs that evaluated hs-cTn assays from 2021 to 2024.
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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 | 0.188 | 0.358 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".