Stray Electrical Currents in Laparoscopic Instruments Used in da Vinci <sup>®</sup> Robot-Assisted Surgery: An <i>In Vitro</i> Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The da Vinci(®) surgical system requires the use of electrosurgical instruments. The re-use of such instruments creates the potential for stray electrical currents from capacitive coupling and/or insulation failure. We used objective measures to report the prevalence and magnitude of such stray currents. MATERIALS AND METHODS: Thirty-seven robotic instruments were tested using an electrosurgical unit (ESU) at pure coagulation and cut waveforms at four different settings. Conductive gel-coated instruments were tested at 40W, 80W, and maximum ESU output (coagulation 120W, cut 300W). The magnitude of stray currents was measured by an electrosurgical analyzer. RESULTS: At coagulation waveform in open air, 86% of instruments leaked a mean of 0.4W. In the presence of gel-coated instruments, stray currents were detected in all instruments with means (and standard deviation) of 3.4W (± 2), 4.1W (± 2.3), and 4.1W (± 2.3) at 40W, 80W, and 120W, respectively. At cut waveform in open air, none of the instruments leaked current, while gel-coated instruments leaked a mean of 2.2W (± 1.3), 2.2W (± 1.9) and 3.2W (± 1.9) at 40W, 80W, and 300W, respectively. CONCLUSIONS: All tested instruments in our study demonstrated energy leakage. Stray currents were higher during coagulation (high voltage) waveforms, and the magnitude was not always proportionate to the ESU settings. Stray currents have the potential to cause electrical burns. We support the programmed end of life of da Vinci instruments on the basis of safety. Consideration should be given to alternate energy sources or the adoption of active electrode monitoring technology to all monopolar instruments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".