Accuracy of Physicians in Differentiating Type 1 and Type 2 Myocardial Infarction Based on Clinical Information
Bibliographic record
Abstract
BACKGROUND: Physicians commonly judge whether a myocardial infarction (MI) is type 1 (thrombotic) vs type 2 (supply/demand mismatch) based on clinical information. Little is known about the accuracy of physicians' clinical judgement in this regard. We aimed to determine the accuracy of physicians' judgement in the classification of type 1 vs type 2 MI in perioperative and nonoperative settings. METHODS: (OPTIMUS) Study, which investigated the prevalence of a culprit lesion thrombus based on intracoronary optical coherence tomography (OCT) in patients experiencing MI. Four MI cases, 2 perioperative and 2 nonoperative, were selected randomly, stratified by etiology. Physicians were provided with the patient's medical history, laboratory parameters, and electrocardiograms. Physicians did not have access to intracoronary OCT results. The primary outcome was the accuracy of physicians' judgement of MI etiology, measured as raw agreement between physicians and intracoronary OCT findings. Fleiss' kappa and Gwet's AC1 were calculated to correct for chance. RESULTS: The response rate was 57% (308 of 536). Respondents were 62% male; median age was 45 years (standard deviation ± 11); 45% had been in practice for > 15 years. Respondents' overall accuracy for MI etiology was 60% (95% confidence interval [CI] 57%-63%), including 63% (95% CI 60%-68%) for nonoperative cases, and 56% (95% CI 52%-60%) for perioperative cases. Overall chance-corrected agreement was poor (kappa = 0.05), consistent across specialties and clinical scenarios. CONCLUSIONS: Physician accuracy in determining MI etiology based on clinical information is poor. Physicians should consider results from other testing, such as invasive coronary angiography, when determining MI etiology.
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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 | 0.021 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".