An Unmodulated Very-Low-Voltage Electrosurgical Technology Creates Predictable and Ultimate Tissue Coagulation: From Experimental Data to Clinical Use
Bibliographic record
Abstract
Objective. We analyzed the underlying principles of an unmodulated very-low-voltage (VLV) mode, designated as “soft coagulation” in hemostasis, and demonstrate its clinical applications. Summary Background Data. While the advantage of the VLV mode has been reported across surgical specialties, the basic principle has not been well described and remains ambiguous. Methods. Characteristics of major electrosurgical modes were measured in different settings. For the VLV mode, the tissue effect and electrical parameters were assessed in simulated environments. Results. The VLV mode achieved tissue coagulation with the lowest voltage compared with the other modes in any settings. With increasing impedance, the voltage of the VLV mode stayed very low at under 200 V compared with other modes. The VLV mode constantly produced effective tissue coagulation without carbonization. We have demonstrated the clinical applications of the method. Conclusions. The voltage of the VLV mode consistently stays under 200 V, resulting in tissue coagulation with minimal vaporization or carbonization. Therefore, the VLV mode produces more predictable tissue coagulation and minimizes undesirable collateral thermal tissue effects, enabling nerve- and function-preserving surgery. The use of VLV mode through better understanding of minimally invasive way of using electrosurgery may lead to better surgical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".