Sharing and Teaching Electrocardiograms to Minimize Infarction (STEMI): reducing diagnostic time for acute coronary occlusion in the emergency department
Bibliographic record
Abstract
BACKGROUND: Limits to ST-Elevation Myocardial Infarction (STEMI) criteria may lead to prolonged diagnostic time for acute coronary occlusion. We aimed to reduce ECG-to-Activation (ETA) time through audit and feedback on STEMI-equivalents and subtle occlusions, without increasing Code STEMIs without culprit lesions. METHODS: This multi-centre, quality improvement initiative reviewed all Code STEMI patients from the emergency department (ED) over a one-year baseline and one-year intervention period. We measured ETA time, from the first ED ECG to the time a Code STEMI was activated. Our intervention strategy involved a grand rounds presentation and an internal website presenting weekly local challenging cases, along with literature on STEMI-equivalents and subtle occlusions. Our outcome measure was ETA time for culprit lesions, our process measure was website views/visits, and our balancing measure was the percentage of Code STEMIs without culprit lesions. RESULTS: There were 51 culprit lesions in the baseline period, and 64 in the intervention period. Median ETA declined from 28.0 min (95% confidence interval [CI] 15.0-45.0) to 8.0 min (95%CI 6.0-15.0). The website garnered 70.4 views/week and 27.7 visitors/week in a group of 80 physicians. There was no change in percentage of Code STEMIs without culprit lesions: 28.2% (95%CI 17.8-38.6) to 20.0% (95%CI 11.2-28.8%). Conclusions Our novel weekly web-based feedback to all emergency physicians was associated with a reduction in ETA time by 20 min, without increasing Code STEMIs without culprit lesions. Local ECG audit and feedback, guided by ETA as a quality metric for acute coronary occlusion, could be replicated in other settings to improve care.
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 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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".