2007 Universal Myocardial Infarction Definition Change Criteria for Risk Stratification by Use of a High-Sensitivity Cardiac Troponin I Assay
Bibliographic record
Abstract
Analytical sensitivity is an important determinant of the diagnostic power of cardiac troponin (cTn)1 for diagnosing myocardial infarction (1)(2). Increased cTn only partially fulfills the criteria to document acute injury, however, because a change (increase/decrease) in biomarker concentration is required (1). A recent study of a contemporary sensitive, guideline-acceptable cTnI assay suggested that change criteria improved risk stratification because it improved the specificity of cTnI in the study for acute coronary syndrome (ACS) (2). Data about the use of change criteria are only beginning to emerge (2). Furthermore, as assay performance improves and more diagnostic companies proceed to develop the second- and third-generation high-sensitivity (hs) assays (3), each must be validated with these criteria. Moreover, there remains no consensus on the criteria to define a changing pattern for cTn assays in clinical use, or for the new hs-cTn assays. One approach has been to use criteria for change obtained from the 2007 universal myocardial infarction (MI) definition (1). This was recently used in a comparison of a sensitive cTnI assay [AccuTnI; refer to Apple (3) for assay designation] against a research third-generation hs assay (hs-cTnI) for early detection of myocardial injury (4). In the present study, we assessed if the criteria for change used in the previous study (4) also add to risk stratification with this hs-cTnI assay.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".