Characterization and significance of localized sources identified by a novel automated algorithm during mapping of human persistent atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Automated algorithms may identify focal (FA) and rotational (RoA) activations during persistent atrial fibrillation (PeAF). OBJECTIVE: To evaluate an automated algorithm for characterizing and assessing significance of FA/RoA. METHODS: Eighty-six PeAF ablation patients (1411 maps) were analyzed. Maps were obtained with a 64-electrode basket using CARTOFINDER, which filters/annotates atrial unipolar electrograms over 30 seconds. Operators ablated FA/RoA followed by pulmonary vein isolation (PVI). The automated algorithm was retrospectively applied using QS patterns to identify FA and sequential activation gradients for RoA without phase mapping. Algorithm-identified FA and RoA were validated against blinded adjudicators. Ablation of algorithm-identified FA/RoA was related to procedural AF termination. RESULTS: 73% ± 18% of electrodes (65% ± 11% atrial surface area) were adequate for analysis. Compared with adjudicators, the algorithm had a sensitivity of 84% for FA and 86% for RoA. There were 4 ± 2 FA and 2 ± 2 RoA per patient. FA occurred 8 ± 6 times during the 30-second window (cumulative duration 8 ± 6 seconds). RoA occurred 5 ± 3 times (median 2, consecutive rotations) with a cumulative duration of 3 ± 2 seconds. Compared to patients without procedural AF termination, patients with termination had more FA ablated (75% vs 38%, P = 0.006). AF termination was not predicted by percentage of RoA ablated although there was a trend towards a higher percentage of left atrial RoA ablated ( P = 0.06). CONCLUSION: An automated algorithm had high sensitivity for FA and RoA. Acute AF termination was associated with FA ablation but not RoA ablation. Future studies need to define the significance of FA and RoA and whether they are overlapping or separate mechanisms.
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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 | 0.005 |
| 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 | 0.001 | 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".