Impact of Switching Sample Types for High-Sensitivity Cardiac Troponin I Assays in the 0/1 Hour Algorithms
Bibliographic record
Abstract
Over the past 20 years there has been much work in understanding the release and processing of the cardiac troponin complex (troponin C, troponin I, troponin T) in the circulation (1, 2). Of particular interest to clinical laboratories are studies that have demonstrated the impact of sample type and the cardiac troponin complex. Here, ethylenediaminetetraacetic acid (EDTA) has been observed to break up the cardiac troponin complex into the individual subunits with thrombin (i.e., serum samples) cleaving cardiac troponin T (1, 2). During the transition from the contemporary cardiac troponin assays to the high-sensitivity cardiac troponin (hs-cTnI or hs-cTnT) assays, manufacturers have opted to limit the number of sample types listed in their package inserts. For example, for the contemporary cTnI assays, both Siemens ADVIA Centaur and Ortho VITROS list serum, lithium heparin plasma, and EDTA plasma as sample types; however for their respective hs-cTnI assays, only serum and lithium heparin plasma are listed as sample types. Since many clinical studies do not specify their sample types or use more than one sample type in their study and analyses, the effects of matrix on concentrations and cutoffs used in rapid algorithms for myocardial infarction are largely unknown. To this end, we assessed the impact of EDTA or lithium heparin plasma versus serum hs-cTnI concentrations on potential misclassification using the published 0/1 h algorithms (3–5).
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.021 | 0.015 |
| 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, 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".