Accelerated diagnostic pathways for myocardial infarction using a Siemens High-Sensitivity cardiac troponin I assay
Bibliographic record
Abstract
BACKGROUND: Few studies have comprehensively examined high-sensitivity cardiac troponin I (hs-cTnI) based diagnostic pathways for myocardial infarction (MI) in early presenters using a Siemens ADVIA Centaur hs-cTnI assay. METHODS: We conducted a prospective multicenter cohort study in Emergency Departments involving 414 patients suspected of MI within 6 h of symptom onset. We evaluated three hs-cTnI-based pathways (High-STEACS, ESC 0/1-h, 0/2-h); and four pathways incorporating medical history and physical findings (ADAPT, EDACS, HEART, GRACE). We evaluated negative predictive value (NPV) and sensitivity as safety measures, and percentage ruled out as an efficiency measure for a primary outcome of type 1 myocardial infarction or cardiac death within 30 days. RESULTS: Median age was 72 years (interquartile range 58-82), and 30.4 % (126/414) of patients were over 80. Females comprised 44.2 % (183/414) of patients, 87.7 % (363/414) had chest pain, and the primary outcome occurred in 9.2 % (38/414). The High-STEACS pathway ruled out 62.0 % of patients without missing a case of an MI. The ESC 0/1-h and 0/2-h pathways showed high NPV and sensitivities; however, they ruled out fewer patients (35.9 % and 45.2 %, respectively). The ADAPT, EDACS, and HEART pathways demonstrated high NPV and sensitivities but ruled out fewer patients (15-27 %). The GRACE pathway missed 2 cases with primary clinical outcomes. Among patients over 80 without MI, initial hs-cTnI concentration was ≥ 3 ng/L in 99.1 % and ≥ 5 ng/L in 84.1 %. CONCLUSIONS: The High-STEACS pathway was the most efficient among the hs-cTnI-based pathways while maintaining excellent safety performance in early presenters.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".