668Principal component analysis can identify ventricular regions with highest variability in the arrhythmogenic substrate of ventricular tachycardia patients
Bibliographic record
Abstract
Abstract Introduction Left ventricle (LV) substrate can be characterized by imaging modalities providing physiological variables (tissue perfusion, ischemia, etc.) grouped in a complex dataset. Principal Component Analysis (PCA) can identify the dataset variables that best explain its variance. Variables linked to substrate arrhythmogenicity will lead PCA to identify LV regions most influenced by the variables on a LV 3D geometry. Purpose To evaluate whether regions identified by PCA correspond to regions targeted by physicians in a ventricular tachycardia (VT) ablation procedure. Methods Ischemic VT subjects underwent SPECT/CT perfusion imaging (rest and stress) prior to LV voltage mapping with the cardiac mapping system. Co-registration allowed projection of ablation sites onto SPECT/CT geometries. PCA retrospectively analyzed the following dataset: tissue perfusion (rest and stress), tissue ischemia and the local (1cm2 sub-regions) stress-rest difference for: 1) standard deviation (STD) and 2) skewness (Skew). PCA components (PCAc) explaining ≥85% of the total variance were plotted on the LV 3D geometry to display regions highly influenced by the dataset variables (see figure below). Results Ten subjects (9 males, 66 ± 8 years old, LVEF 37 ± 11%) underwent co-registration. In 7/10 subjects, tissue perfusion (in PCAc#1) and ischemia (in PCAc#2) were most influential on LV regions variance. Ischemia and low perfusion (≤40%) areas equaled 32 ± 19 % of the LV with 21 ± 23% of these areas highly involved in the arrhythmogenic substrate (≥75% of explained LV variance). The location of 63 ± 18% of ablation sites were <1 cm from areas explaining ≥50% of LV variance. Conclusion Preliminary results showed that PCA can synthesize the influence of different perfusion derived variables, like tissue perfusion and ischemia, used in SPECT/CT imaging of the LV. Further analysis is needed to confirm whether this could be used to select VT ablation targets. Abstract Figure.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".