Correlation of Antemortem Serum Creatine Kinase, Creatine Kinase-MB, Troponin I, and Troponin T with Cardiac Pathology
Bibliographic record
Abstract
BACKGROUND: Spurious increases in serum troponins, especially troponin T, have been reported in patients with and without acute myocardial syndromes. METHODS: We studied 78 autopsied patients without clinical myocardial infarction (MI) and correlated histologic cardiac findings with antemortem serum creatine kinase (CK), its MB isoenzyme (CK-MB), cardiac troponin I (cTnI), and cardiac troponin T (cTnT). RESULTS: There was no significant myocardial pathology in 15 patients. Cardiac pathologies were in five groups: scarring from previous MI or patchy ventricular fibrosis (n = 9), recent MI (n = 27), healing MI (n = 7), degenerative myocyte changes consistent with congestive heart failure (CHF; n = 12), and other cardiac pathologies (n = 8). The median concentrations in the five groups were not significantly different for either CK or CK-MB. Compared with the no-pathology group, only the MI group was significantly different for cTnI, and the MI and other pathology groups were significantly different for cTnT. For patients with MI, 22%, 19%, 48%, and 65% had increased CK, CK-MB, cTnI, and cTnT, respectively; for CHF and other cardiac pathologies combined, the percentages were 28%, 17%, 22%, and 50%. For patients with increased cTnI, 72% and 28% had MI and other myocardial pathologies, respectively; patients with increased cTnT had 64% and 36%, respectively. Patients without myocardial pathology had no increases in CK-MB, cTnI, or cTnT. CONCLUSIONS: All patients with increased serum CK-MB, cTnI, and cTnT had significant cardiac histologic changes. The second-generation cTnT assay appears to be a more sensitive indicator of MI and other myocardial pathologies than the cTnI assay used in this study.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".