Eliminating the effects of motion during radiofrequency lesion delivery using a novel contact‐force controller
Bibliographic record
Abstract
INTRODUCTION: Catheter-tissue contact force is a determinant of radiofrequency (RF) ablation lesion effectiveness. However, ablation on a beating heart is subject to force variability, making it difficult to optimally deliver consistently durable and transmural lesions. This work evaluates improvements in contact force stability and lesion reproducibility by using a catheter contact-force controller (CFC) during lesion delivery in vitro and in vivo. METHODS AND RESULTS: Using a sheath and force-sensing catheter, an experienced operator attempted to maintain a constant force of 20 g at targets within the atria and left ventricle of a pig manually and using the CFC; the average force and contact-force variation (CFV) achieved using each approach were compared. Ablation lesions (20 W, 30 seconds, 17 mL/min irrigation) were created in bovine tissue samples mounted on a platform programmed to reproduce clinically relevant motion. CFC-assisted lesions were delivered to stationary and moving tissue with forces of 5 to 35 g. Mimicking manual intervention, lesions were also delivered to moving tissue while the CFC was disabled. Resultant lesion volumes were compared using two-way analysis of variance. When using the CFC, the average force was within 1 g of the set level, with a CFV less than 5 g, during both in vitro and in vivo experiments. Reproducible and statistically identical (P = .82) lesion volumes proportional to the set force were achieved in both stationary and moving tissue when the CFC was used. CONCLUSIONS: CFC assistance maintains constant force in vivo and removes effect of motion on lesion volume during RF lesion delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".