External Validation and Extension of a Clinical Score for the Discrimination of Type 2 Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: The early non-invasive discrimination of Type 2 versus Type 1 Myocardial Infarction (T2MI, T1MI) is a major unmet clinical need. We aimed to externally validate a recently derived clinical score (Neumann) combing female sex, no radiating chest pain, and high-sensitivity cardiac troponin I (hs-cTnI) concentration ≤40.8 ng/L. METHODS: Patients presenting with acute chest discomfort to the emergency department were prospectively enrolled into an international multicenter diagnostic study. The final diagnoses of T2MI and T1MI were centrally adjudicated by two independent cardiologists using all information including cardiac imaging and serial measurements of hs-cTnT/I according to the fourth universal definition of MI. Model performance for T2MI diagnosis was assessed by formal tests and graphical means of discrimination and calibration. RESULTS: < 0.001) and had good calibration. Patients with the highest score values of 3 (Neumann Score, 9.9%) and 5 (Extended Neumann Score, 3.3%) had a 53% and 91% predicted probability of T2MI, respectively. CONCLUSION: The Neumann Score provided moderate discrimination and suboptimal calibration. Extending the Neumann Score by adding heart rate >120/min improved the model's performance.
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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 | 0.059 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".