Troponin: the biomarker of choice for the detection of cardiac injury
Bibliographic record
Abstract
It has been known for 50 years that transaminase activity increases in patients with acute myocardial infarction. With the development of creatine kinase (CK), biomarkers of cardiac injury began to take a major role in the diagnosis and management of patients with acute cardiovascular disease. In 2000 the European Society of Cardiology and the American College of Cardiology recognized the pivotal role of biomarkers and made elevations in their levels the “cornerstone” of diagnosis of acute myocardial infarction. At that time, they also acknowledged that cardiac troponin I and T had supplanted CK-MB as the analytes of choice for diagnosis. In this review, we discuss the science underlying the use of troponin biomarkers, how to interpret troponin values properly and how to apply these measurements to patients who present with possible cardiovascular disease. Troponin is the biomarker of choice for the detection of cardiac injury. To use it properly, one must understand how sensitive the specific assay being used is for detecting cardiac injury, the fact that elevated troponin levels are highly specific for cardiac injury and some critical issues related to the basic science of the protein and its measurement. In this article, we review the biology of troponin, characteristics of assays that measure serum troponin levels and how to apply these measurements to patients who present with possible cardiovascular disease. We also discuss other clinical situations in which troponin levels may be elevated.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".