Body Surface Mapping Improves Diagnosis of Acute Myocardial Infarction in the Emergency Department
Bibliographic record
Abstract
Traditionally, the diagnosis of acute myocardial infarction (AMI) in emergency departments is done through an assessment of history and presenting symptoms, 12-lead electrocardiogram (ECG), and cardiac biomarkers. The 12-lead ECG is not highly sensitive for detecting ECG changes, and some infarctions may be missed. Failure to identify patients in the early stages of AMI can result in failure to provide beneficial therapies. New technology, the 80-lead ECG, uses body surface mapping to provide a more comprehensive view of cardiac electrical activity. Body surface mapping has greater sensitivity in detecting AMI in the inferoposterior portions of the left ventricle and the right ventricle. Portable hardware and user-friendly software coupled with an easily applied disposable torso vest containing the electrodes produce a 12-lead ECG, 80-lead ECG, and color contour torso or flat map showing ECG changes. Recent studies support the use of 80-lead body surface mapping for detecting AMI in the emergency department.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".