Error reporting from the da Vinci surgical system in robotic surgery: A Canadian multispecialty experience at a single academic centre
Bibliographic record
Abstract
INTRODUCTION: The goal of the study is to evaluate and report on the third-generation da Vinci surgical (Si) system malfunctions. METHODS: A total of 1228 robotic surgeries were performed between January 2012 and December 2015 at our academic centre. All cases were performed by using a single, dual console, four-arm, da Vinci Si robot system. The three specialties included urology, gynecology, and thoracic surgery. Studied outcomes included the robotic surgical error types, immediate consequences, and operative side effects. Error rate trend with time was also examined. RESULTS: Overall robotic malfunctions were documented on the da Vinci Si systems event log in 4.97% (61/1228) of the cases. The most common error was related to pressure sensors in the robotic arms indicating out of limit output. This recoverable fault was noted in 2.04% (25/1228) of cases. Other errors included unrecoverable electronic communication-related in 1.06% (13/1228) of cases, failed encoder error in 0.57% (7/1228), illuminator-related in 0.33% (4/1228), faulty switch in 0.24% (3/1228), battery-related failures in 0.24% (3/1228), and software/hardware error in 0.08% (1/1228) of cases. Surgical delay was reported only in one patient. No conversion to either open or laparoscopic occurred secondary to robotic malfunctions. In 2015, the incidence of robotic error rose to 1.71% (21/1228) from 0.81% (10/1228) in 2014. CONCLUSIONS: Robotic malfunction is not infrequent in the current era of robotic surgery in various surgical subspecialties, but rarely consequential. Their seldom occurrence does not seem to affect patient safety or surgical outcome.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".