Local abnormal ventricular activity detection in scar‐related VT: Microelectrode versus conventional bipolar electrode
Bibliographic record
Abstract
BACKGROUND: Conventional bipolar electrodes (CBE) may be suboptimal to detect local abnormal ventricular activities (LAVAs). Microelectrodes (ME) may improve the detection of LAVAs. This study sought to elucidate the detectability of LAVAs using ME compared with CBE in patients with scar-related ventricular tachycardia (VT). METHODS: We included consecutive patients with structural heart disease who underwent radiofrequency catheter ablation for scar-related VT using either of the following catheters equipped with ME: QDOTTM or IntellaTip MIFITM. Detection field of LAVA potentials were classified as three types: Type 1 (both CBE and ME detected LAVA), Type 2 (CBE did not detect LAVA while ME did), and Type 3 (CBE detected LAVA while ME did not). RESULTS: In 16 patients (68 ± 16 years; 14 males), 260 LAVAs electrograms (QDOT = 72; MIFI = 188) were analyzed. Type 1, type 2, and type 3 detections were 70.8% (QDOT, 69.4%; MIFI, 71.3%), 20.0% (QDOT, 23.6%; MIFI, 18.6%) and 9.2% (QDOT, 6.9%; MIFI, 10.1%), respectively. The LAVAs amplitudes detected by ME were higher than those detected by CBE in both catheters (QDOT: ME 0.79 ± 0.50 mV vs. CBE 0.41 ± 0.42 mV, p = .001; MIFI: ME 0.73 ± 0.64 mV vs. CBE 0.38 ± 0.36 mV, p < .001). CONCLUSIONS: ME allow to identify 20% of LAVAs missed by CBE. ME showed higher amplitude LAVAs than CBE. However, 9.2% of LAVAs can still be missed by ME.
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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 | 0.003 |
| 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) | 0.001 | 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".