Identification of patients at risk for ventricular tachycardia by means of body surface potential maps
Bibliographic record
Abstract
In this work we attempted the stratification of patients at risk for VT by means of BSPM recorded during sinus rhythm, based on the evidence that specific arrhythmogenic alterations manifest on body surface potentials. Due to the high dimensionality of BSPM data and the limited number of available patients, a feature extraction step was necessary prior to the classifier design. Feature extraction was performed by means of linear expansions of two different time intervals: QRS and ST-T complexes. Two approaches were studied: the Karhunen-Love transform (KLT) and spatio-temporal expansions. A multivariate linear discriminant analysis was applied to the extracted features to classify the study population in two groups: VT and non-VT. Our results showed that spatio-temporal features (SE = 83%, SP = 86%) obtained similar classification results than KLT features (SE = 78%, SP = 93%) with a lower computational cost. For comparison, a method reported in the literature based on QRST integral maps was implemented, obtaining results within the same range (SE = 88%, SP = 72%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".