Novel Biomarkers for Acute Myocardial Infarction: Is MicroRNA the New Kid on the Block?
Bibliographic record
Abstract
Early detection of acute myocardial infarction (AMI)9 is crucial for deciding the course of treatment to preserve and prevent further damage to the myocardial tissue. Guidelines from the American College of Cardiology give the criteria for AMI as an increase or decrease in troponin with at least one value >99% percentile of a healthy population (with an imprecision of <10%) accompanied by either symptoms of ischemia or evidence of MI based on imaging or electrocardiography modalities. Over the last several years, creatine kinase MB testing has been replaced by newer generations of troponin assays. High-sensitivity assays are now considered the gold-standard marker for myocardial necrosis. Indeed, the improved analytical sensitivity of high-sensitivity troponin assays has lowered the threshold for myocardial injury, making it easier to detect baseline values in the picogram-per-milliliter range and thus expediting early diagnosis. The gains in diagnostic sensitivity have come at the cost of decreases in diagnostic specificity, however. A major challenge is that high-sensitivity assays quantify troponin in a greater proportion of healthy individuals. These assays also detect myocardial cell death associated with other pathophysiological conditions, such as cardiomyopathies, myocarditis, renal failure, congestive heart failure, and pulmonary embolism, and do not distinguish between mechanisms of tissue injury, thus limiting the ability to differentiate on the basis of MI severity [e.g., ST-segment elevation myocardial infarction (STEMI) vs. non-STEMI]. Consequently, the introduction of high-sensitivity troponin assays into clinical practice has caused confusion for physicians treating patients with acute chest pain. Therefore, the ideal biomarker to rule in and rule out AMI rapidly and reliably is still lacking.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".