Cardiac Biomarkers for Detection of Myocardial Infarction: Perspectives from Past to Present
Bibliographic record
Abstract
With great pleasure and anticipation in recognition of Clinical Chemistry's 50th anniversary, I have been able to arm-twist four talented scientists to document their impressive marks on the science of diagnostics in the field of cardiac biomarkers and detection of myocardial infarction. Their exciting discoveries and applications have dramatically influenced the fields of laboratory medicine and cardiology and have greatly influenced the care and management of thousands of patients suffering from coronary artery disease leading to acute myocardial infarction. As a matter of historical record, I owe a great deal of thanks to each one of the coauthors of this special report because each one has personally influenced my scientific career. I met Dr. Rosalki, during my postdoctoral training, at a national AACC meeting, where he kindly answered my numerous queries regarding creatine kinase enzymology and muscle physiology. Dr. Roberts, while serving as Director of the Coronary Care Unit at Washington University in St. Louis, generously allowed this fledgling fellow into his laboratory and shared many of his clinical and experimental findings with me. Dr. Katus, whom I first met at a scientific meeting sponsored by Boehringer Mannheim in 1986 in Bavaria, where I first became fascinated with cardiac troponin T, has remained a friend and colleague. Lastly, Dr. Ladenson, who as mentor, scientific colleague, and close friend remains ultimately responsible for both my professional growth as a clinical chemist (he was my postdoctoral fellowship advisor) and for stimulating and encouraging my goals and aspirations in the field of cardiac biomarkers. With the descriptions of the ground-breaking science described below, I am extremely excited and optimistic that the future of cardiac biomarkers is secure and open to new discoveries by the Rosalkis, Robertses, Katuses, and Ladensons of the future.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| 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".