PREdiction of ST deviations in lead aVR as a noninvasive tool to predict the infarct-Related coronary artery in patients with acute Inferior-wall Myocardial Infarction (The PREST-RIMI Study)
Bibliographic record
Abstract
BACKgROunD: Acute myocardial infarction is one of the most common presentations of coronary artery disease (CAD). Although coronary angiography remains the gold standard for identification of the infarctrelated artery (IRA), conventional 12-lead electrocardiography (ECG) is an essential tool for diagnosis, risk stratification and prognosis. If specific ECG patterns can be recognized, it will be possible to determine the IRA and size of the ventricular area that is jeopardized. The existing ECG algorithms have good sensitivity for the right coronary artery (RCA) and good specificity for the left circumflex artery (LCx) as predictors of IRA in patients with acute inferior-wall myocardial infarction (IWMI), while the specificity for the RCA and sensitivity for the LCx are modest. OBjECTIVE: To evaluate deviations in lead aVR to predict IRA in patients with IWMI, and to validate several commonly used 12-lead ECG characteristics. METhODS: A total of 585 consecutive patients with a first occurrence of acute IWMI were analyzed for the association between ECG and IRA diagnosed using coronary angiography. Subsequently, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of depression in lead aVR, along with various commonly used ECG criteria for predicting the IRA, were estimated using coronary angiographic findings as the gold standard.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".