Diagnostic classification of patients with ventricular tachycardia, based on spatial and temporal features derived from body-surface potential maps
Bibliographic record
Abstract
The authors evaluated the ability of spatial features derived from electrocardiographic QRST-area distributions, and of temporal electrocardiographic measures (heart rate, QRS duration and corrected QT interval), to identify patients at risk for ventricular arrhythmias. Electrocardiograms from 120 leads were recorded simultaneously during sinus rhythm for 102 patients who had had ventricular tachycardia (VT) and for 102 patients who had had a myocardial infarction (MI) but no history of arrhythmias. The Karhunen-Loeve (K-L) transform was used to reduce the QRST-integral maps to 16 coefficients. The best features for discriminating between the two groups were selected by stepwise discriminant analysis, and bootstrap method was used to estimate diagnostic performance on a prospective population. With a set of 8 K-L features, the test-set sensitivity was 90.3/spl plusmn/4.3% and specificity was 78.0/spl plusmn/6.1%. When QRS duration was added as a supplementary feature to the 8 K-L coefficients, specificity increased to 80.9/spl plusmn/5.4%. All three temporal features are highly correlated among themselves; therefore, only one suffices to supplement K-L coefficients.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".