Effect of Repeat Measurements of High-Sensitivity Cardiac Troponin on the Same Sample Using the European Society of Cardiology 0-Hour/1-Hour or 2-Hour Algorithms for Early Rule-Out and Rule-In for Myocardial Infarction
Bibliographic record
Abstract
To the Editor: There is debate on the clinical applicability of the European Society of Cardiology (ESC)1 0/1 h algorithm to rule-out and rule-in myocardial infarction (MI) using high-sensitivity cardiac troponin (hs-cTn) assays (1). External validations have not achieved the same diagnostic accuracy as studies referenced in the ESC guidelines (2). A critique applicable to all early rule-out/rule-in algorithms is whether the precision of hs-cTn assays is sufficient to achieve diagnostic accuracy at the values proposed (1). If not, approaches such as the 2 h algorithm might be more robust, although the latter also may employ changes that are small enough to challenge the analytical variation of assays (3, 4). Our objective was to evaluate the analytical variation of results in the same samples measured 3 times within 3.5 h and to determine the misclassification rate associated with the ESC 0/1 h algorithm and the 2 h algorithm due to short-term analytical variation. Briefly, 50 fresh centrifuged lithium heparin samples (stored at room temperature; not frozen) were measured for hs-cTnI (Abbott Diagnostics) as the first measurement (reported as a whole number, ng/L). The sample was reanalyzed again approximately 1.5 h later (second measurement) and a third measurement approximately 1.5 h after the second measurement …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".