High-density evaluation of the arrhythmogenic substrate in persistent atrial fibrillation
Bibliographic record
Abstract
Background Left atrial (LA) fibrosis is a key component of arrhythmogenic remodelling in atrial fibrillation (AF). LA low-voltage areas (LVAs) are considered surrogates for fibrosis and novel targets for ablation. However, there are no established criteria for identifying such potential pathogenic areas, particularly when utilising omnipolar mapping (OT). Objective To evaluate the correlation between OT and conventional bipolar voltage (BiV) in AF and regular rhythms. Methods Bipolar and OT mapping was performed in 17 patients undergoing de novo ablation for persistent AF. Mapping was performed in AF and coronary sinus pacing (CSP) at 600ms. BiV <0.5mV were defined as low voltage. Results LA voltage in AF correlated poorly with CSP using either BiV (r=0.15), or OT (r=0.16). OT yielded higher voltages than BiV in AF (0.62±0.24 vs. 0.49±0.18mV, p<0.050) and during CSP (1.85±0.78 vs. 1.60±0.80mV, p<0.050). LVA burden, as percentage of LA surface area, varied significantly depending on the atrial rhythm and mapping approach (AF bipolar: 65.0 ± 15.6%, AF OT: 56.2 ± 17.0%, CSP bipolar: 34.2±18.9%, CSP OT: 24.56±13.5%, p<0.050). BiV thresholds of 0.5mV during CSP and 0.3mV in AF corresponded to an OT voltage of 0.84mV and 0.40mV, respectively. Conclusion The mapping tool and atrial rhythm significantly influence LA voltage and LVA burden for both bipolar and OT mapping. Applying a universal bipolar or OT cut-off for low voltage in AF and sinus rhythm will not accurately reflect the arrhythmogenic substrate. OT yields higher voltage than corresponding bipolar measurements, thus adjustments in thresholds are required when using OT.
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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 | 0.000 | 0.001 |
| 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".