New Body Surface Isopotential Map Evaluation Method to Detect Minor Potential Losses in Non–Q-Wave Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Potential losses caused by stable non-Q-wave myocardial infarction (MI) are too small to diagnose with the use of standard ECG. The aim of the present study was to obtain accurate diagnostic criteria for this prognostically important disease with the help of body surface mapping. METHODS AND RESULTS: Body surface potentials were recorded with the use of 63 unipolar leads in 45 patients with a non-Q-wave MI (41 to 75 years old); 24 healthy adults, 42 patients with unstable angina, and 70 patients with Q-wave MI served as reference groups. Qualitative pathological features of the isopotential maps, such as onset time and site and magnitude of the first right-anterior/anterior minimum, as well as pathological negativities at that time, were defined in non-Q-wave MI cases. These features, which account for the activation sequence and the body surface projections of specific cardiac regions (Selvester classification), showed a 91% sensitivity and an 88% specificity for the detection of non-Q-wave MI. In comparison, the different departure maps (first third QRS, QRS, and QRST isoarea) resulted in less favorable specificities (50% to 58%). Concordance between the isopotential maps and the acute-phase ECG (90%), hypokinesis (64%), fixed perfusion defects (59%), and significant stenosis of the infarct-related coronary artery (87%) supported the concept that these isopotential map changes correspond to the supposed sites of MI. There were pathological features in 69% of patients with unstable angina, with similar concordances as in non-Q-wave MI. CONCLUSIONS: Isopotential maps revealed characteristic features that were suitable for the detection and localization of non-Q-wave MI in the clinical setting of unstable coronary artery disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".