A 0/1h-algorithm using cardiac myosin-binding protein C for early diagnosis of myocardial infarction
Bibliographic record
Abstract
AIMS: Cardiac myosin-binding protein C (cMyC) demonstrated high diagnostic accuracy for the early detection of non-ST-elevation myocardial infarction (NSTEMI). Its dynamic release kinetics may enable a 0/1h-decision algorithm that is even more effective than the ESC hs-cTnT/I 0/1 h rule-in/rule-out algorithm. METHODS AND RESULTS: In a prospective international diagnostic study enrolling patients presenting with suspected NSTEMI to the emergency department, cMyC was measured at presentation and after 1 h in a blinded fashion. Modelled on the ESC hs-cTnT/I 0/1h-algorithms, we derived a 0/1h-cMyC-algorithm. Final diagnosis of NSTEMI was centrally adjudicated according to the 4th Universal Definition of Myocardial Infarction. Among 1495 patients, the prevalence of NSTEMI was 17%. The optimal derived 0/1h-algorithm ruled-out NSTEMI with cMyC 0 h concentration below 10 ng/L (irrespective of chest pain onset) or 0 h cMyC concentrations below 18 ng/L and 0/1 h increase <4 ng/L. Rule-in occurred with 0 h cMyC concentrations of at least 140 ng/L or 0/1 h increase ≥15 ng/L. In the validation cohort (n = 663), the 0/1h-cMyC-algorithm classified 347 patients (52.3%) as 'rule-out', 122 (18.4%) as 'rule-in', and 194 (29.3%) as 'observe'. Negative predictive value for NSTEMI was 99.6% [95% confidence interval (CI) 98.9-100%]; positive predictive value 71.1% (95% CI 63.1-79%). Direct comparison with the ESC hs-cTnT/I 0/1h-algorithms demonstrated comparable safety and even higher triage efficacy using the 0h-sample alone (48.1% vs. 21.2% for ESC hs-cTnT-0/1 h and 29.9% for ESC hs-cTnI-0/1 h; P < 0.001). CONCLUSION: The cMyC 0/1h-algorithm provided excellent safety and identified a greater proportion of patients suitable for direct rule-out or rule-in based on a single measurement than the ESC 0/1h-algorithm using hs-cTnT/I. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT00470587.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".