Effect of Real-Time Physician Oversight of Prehospital STEMI Diagnosis on ECG-Inappropriate and False Positive Catheterization Laboratory Activation
Bibliographic record
Abstract
BACKGROUND: ST-elevation myocardial infarction diagnosis at first medical contact (FMC) and prehospital cardiac catheterization laboratory (CCL) activation are associated with reduced total ischemic time and therefore have become the dominant ST-elevation myocardial infarction referral method in primary percutaneous coronary intervention systems. We sought to determine whether physician oversight was associated with improved diagnostic performance in a prehospital CCL activation system and what effect the additional interpretation has on treatment delay. METHODS: Between 2012 and 2015, all patients in 2 greater Montreal catchment areas with a chief symptom of chest paint or dyspnea had an in-the-field electrocardiogram (ECG). A machine diagnosis of "acute myocardial infarction" resulted either in automatic CCL (automated cohort without oversight) or transmission of the ECG to the receiving centre emergency physician for reinterpretation before CCL activation. System performance was assessed in terms of the proportion of false positive and inappropriate activations (IA), as well as the proportion of patients with FMC-to-device times ≤ 90 minutes. RESULTS: < 0.001). There was no significant effect on the proportion of false positive activation. CONCLUSIONS: Real-time physician oversight might be associated with fewer IAs, but also appears to have a deleterious effect on FMC-to-device performance. Identifying predictors of IA could improve overall performance by selecting ECGs that merit physician oversight and streamlining others. Larger clinical studies are warranted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".