Detection of acute myocardial ischemia by vessel-specific leads derived from reduced lead sets
Bibliographic record
Abstract
The aim of this study was to evaluate whether detection performance of ACC/ESC STEMI criteria based on 12-lead ECG can be improved by using criteria from 3 vessel-specific leads (VSLs) derived from reduced lead sets of 12-lead ECG using 3 limb electrodes at Masson-Likar torso sites and 2 chest electrodes at precordial sites V1 to V6. There are a total of 15 such lead sets and each can be recorded using a 6-wire ECG cable. The VSLs were derived from a large 120-lead mapping database (n = 892). Study data consisted of 12-lead ECGs acquired during 99 angioplasty-induced ischemic episodes with prolonged balloon inflation (mean inflation time 4′37″) in 35 LAD, 47 RCA, and 17 LCx coronary arteries. The results, which will need validation on larger patient population, suggest that VSLs derived from several reduced lead sets of standard 12-lead ECG can perform better in acute myocardial ischemia identification. For the highest ranked subset (leads II, III, V3, and V6) for all occlusion subgroups the increase in sensitivity was 15.5% for the total population and 17.4%, 6.1%, and 34.7% for LAD, RCA, and LCx subgroups, respectively without any decrease in specificity. Thus it appears that VSLs derived from reduced leads of 12-lead ECG can be used to improve detection of ischemia, especially those caused by the LCx artery occlusion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".