Distinction between myocardial ischemia and postural changes in continuous ECG monitoring based on ST-segment amplitude and vector orientation--preliminary results.
Bibliographic record
Abstract
BACKGROUND: Myocardial ischemia, commonly defined as ST-segment elevation or depression on the electrocardiogram (ECG), is plagued by a large number of false positive events. OBJECTIVES: To present a new method that attempts to distinguish between 'highly probable ischemia' and positional changes. METHODS: Continuous three-lead orthogonal ECG monitoring was performed in three groups of subjects: 16 healthy volunteers undergoing a body position change protocol, 22 patients undergoing percutaneous transluminal coronary angioplasty (PTCA) and 17 patients with acute coronary syndromes (ACS). For each event (ischemic or postural), the change in ST-segment amplitude was calculated, as well as the angle between the ST-segment vector of the reference beat and the beats demonstrating ST-segment elevation or depression. Angles and ST-segment amplitude changes from well-documented ischemic events obtained from the PTCA patients and from the healthy volunteers in six different body positions were compared. RESULTS: Using both ST-segment amplitude and vector angle changes, ischemic events could be detected and differentiated from a postural change with a sensitivity of 91% and a specificity of 96%. Finally, the approach was blindly applied to continuous ECG recordings of ACS patients. The method allowed the classification of 37% of all ST-segment changes detected as highly probable ischemic events as opposed to only 7% using the standard 100 microV threshold. CONCLUSION: The current approach showed that highly probable ischemic events could be better distinguished from positional changes with objective criteria using ST-segment amplitude and vector orientation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".