Enhancing efficiency in ESD: a comparative analysis of ERBE VIO3 and 300d electrosurgical units
Bibliographic record
Abstract
Abstract Aims Electrosurgical units (ESUs) are essential for tissue dissection hemostasis during ESD. The ERBE VIO 3, enables rapid setting changes, facilitating the swift application of vessel sealing current. Additionally, features such as PreciseSect mode allow dynamic modulation frequency adjustment, making it suitable for submucosal dissection and vessel management. Our comparison of the ERBE VIO3 and 300d aims to assess whether these functionalities enhance the ESD experience. Methods From 2021 to 2024, 88 patients undergoing ESD for colorectal lesions were identified from a prospectively maintained database. Lesions were categorized based on the ESU utilized. Results Eighty-eight procedures were identified. Forty-four (50.0%) procedures were performed using VIO 3 and 44 (50.0%) using VIO 300d. 40 (45.5%) lesions were colonic and 48 (54.5%) rectal. Median lesion diameter was 4.5 cm. Lesions in the VIO3 group were significantly larger (P = 0.027). All ESDs were completed en bloc. Use of the VIO3 resulted in a significantly fewer uses of coagulation graspers overall (28 vs 23, P < 0.001), fewer uses of coagulation graspers for arterial bleeding (1 vs 2, P < 0.001), fewer uses of coagulation graspers per cm2 (0.17 vs 0.58, P < 0.001), and fewer uses of coagulation graspers per minute (0.011 vs 0.066, P < 0.001). This led to a non-significant trend in increased efficiency with use of the VIO3 (4.6 vs 5.1 min/cm2, P = 0.667). Conclusions The VIO 3 significantly decreased reliance on coagulation graspers, particularly in addressing arterial bleeding. This holds the potential to enhance procedural efficiency, reduce bleeding, and lower costs associated with coagulation graspers usage.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".