Characterization of temporal electrical activity patterns for detection of critical isthmus regions of recurrent atypical atrial flutter
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Bibliographic record
Abstract
INTRODUCTION: Identifying the critical isthmus region (CIR) of atrial re-entry tachycardias (AT) is challenging. The Lumipoint® (LP) software, developed for the Rhythmia® mapping system, aims to facilitate the successful ablation of ATs by identifying the CIR. OBJECTIVE: The objective of this study was to evaluate the quality of LP regarding the percentage of arrhythmia-relevant CIR in patients with atypical atrial flutter (AAF). METHODS: In this retrospective study, we analyzed 57 AAF forms. Electrical activity (EA) was mapped over tachycardia cycle length resulting in a two-dimensional EA pattern. The hypothesis was that EA minima suggest potential CIRs with slow-conduction-zone. RESULTS: A total of n = 33 patients were included, with the majority of patients being already preablated (69.7%). LP algorithm identified a mean of 2.4 EA minima and 4.4 suggested CIRs per AAF form. Overall, we observed a low probability of identifying only the relevant CIR (POR) at 12.3% but a high probability that at least one CIR is detected (PALO) at 98.2%. Detailed analysis revealed EA minima depth (≤20%) and width (>50 ms) as the best predictors of relevant CIRs. Wide minima occurred rarely (17.5%), while low minima were more frequently present (75.4%). Minima depth of EA ≤ 20% showed the best PALO/POR overall (95% and 60%, respectively). Analysis in recurrent AAF ablations (five patients) revealed that CIR in de novo AAF was already detected by LP during the index procedure. CONCLUSION: The LP algorithm provides an excellent PALO (98.2%), but poor POR (12.3%) to detect the CIR in AAF. POR improved by preselection of the lowest and widest EA minima. In addition, there might be the role of initial bystander CIRs becoming relevant for future AAFs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 it