Electrosurgical injuries during robot assisted surgery: insights from the FDA MAUDE database
Bibliographic record
Abstract
Introduction: 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 with subsequent injury. The morbidity of such injuries may negate many of the benefits of minimally invasive surgery. We sought to evaluate the rate and nature of electrosurgical injury (ESI) associated with this device. Methods: The Manufacturer and User Facility Device Experience (MAUDE) database is administered by the US Food and Drug Administration (FDA) and reports adverse events related to medical devices in the United States. We analyzed all incidents in the context of robotic surgery between January 2001 and June 2011 to identify those related to the use of electrosurgery. Results: In the past decade, a total of 605 reports have been submitted to the FDA with regard to adverse events related to the da Vinci robotic surgical platform. Of these, 24 (3.9%) were related to potential or actual ESI. Nine out of the 24 cases (37.5%) resulted in additional surgical intervention for repair. There were 6 bowel injuries of which only one was recognized and managed intra-operatively. The remainder required laparotomy between 5 and 8 days after the initial robotic procedure. Additionally, there were 3 skin burns. The remaining cases required conservative management or resulted in no harm. Conclusion: ESI in the context of robotic surgery is uncommon but remains under-recognized and under-reported. Surgeons performing robot assisted surgery should be aware that ESI can occur with robotic instruments and vigilance for intra- and post-operative complications is paramount.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".