Using ECG-To-Activation Time to Assess Emergency Physicians’ Diagnostic Time for Acute Coronary Occlusion
Bibliographic record
Abstract
BACKGROUND: There is no quality metric for emergency physicians' diagnostic time for acute coronary occlusion. OBJECTIVE: We sought to quantify diagnostic time associated with automated interpretation, classic ST-elevation myocardial infarction (STEMI) criteria, STEMI-equivalents, and subtle occlusions, using electrocardiogram (ECG)-to-activation of catheterization laboratory time. METHODS: This multicenter retrospective study reviewed all code STEMI patients from the emergency department (ED) with confirmed culprit lesions from January 2016 to December 2018. We measured door-to-ECG (DTE) time and ECG-to-activation (ETA) time. We examined the first ED ECGs to determine whether automated interpretation labeled "STEMI," and they met classic STEMI criteria, STEMI-equivalents, or rules for subtle occlusion. ECG analysis was performed by two emergency physicians blinded to clinical scenario, automated interpretation, and angiographic outcome. RESULTS: There were 177 code STEMIs with culprit lesions, with a median DTE time of 9.0 min and a median ETA time of 16.0 min. Automated interpretation labeled 55.4% of first ECGs "STEMI" (ETA 6.5 min) and 44.6% not "STEMI" (ETA 66 min, p < 0.0001). Of first ECGs, 63.8% met classic STEMI criteria (ETA 8.0 min), 8.5% had STEMI-equivalents (ETA 32.0 min, p = 0.0026), 16.4% had subtle occlusions (ETA 89.0 min, p = 0.045), and 11.3% had no diagnostic sign of occlusion (ETA 68.0 min, p = 0.20). CONCLUSIONS: STEMI criteria missed more than one-third of occlusions on first ECG, but most had STEMI-equivalents or rules for subtle occlusion. ETA time can serve as a quality metric for emergency physicians to promote new ECG insights and assess quality improvement initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.010 | 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".