Best Practices for Monitoring Cardiac Troponin in Detecting Myocardial Injury
Bibliographic record
Abstract
Cardiac troponin [cardiac troponin I (cTnI) and cTnT] has become globally recognized as the standard biomarker for the diagnosis of acute myocardial infarction (AMI).8 With improvements in the analytical characteristics of cardiac troponin assays, particularly imprecision at low measurable concentrations, high-sensitivity cardiac troponin (hs-cTn) assays are now being implemented worldwide; but not in the US since the Food and Drug Administration has not yet cleared them for clinical use. With the implementation of hs-cTn assays, improvements in clinical care are beginning to be observed in the peer-reviewed literature. These improvements include early rule out (early hospital discharge) and rule in (right bed for appropriate patient) for MI and improved risk stratification for patients presenting with symptoms suggestive of ischemia, with improved short- and long-term outcomes. Further hs-cTn assays have been part of solidifying the definition of myocardial injury, based on an increased cardiac troponin concentration above the 99th percentile upper reference limit. The high-sensitivity assays have improved the clinical understanding that not all cardiac troponin increases are MI and that patients with nonischemic disease also have increases in cardiac troponin that must be managed accordingly. This Q&A provides the opportunity for 3 cardiologists, 2 laboratory medicine scientists, and 1 emergency medicine physician to share their experiences with the evolving role of cardiac troponin testing in their practices. Ideally, the messages they share will assist in better harmonizing the appropriate utilization of high-sensitivity assays worldwide as we transition away from contemporary cardiac troponin assays. Should all medical centers have a uniform serial order set to assist in ruling in/out AMI? Should a single cardiac troponin order be available? What would your ideal serial order set (timing) be? Allan S. Jaffe: Medical centers would benefit from developing consistent, uniform serial orders to rule in/out AMI. These serial orders should be agreed …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".