Robotic assisted orbital surgery for resection of advanced periocular tumours – a case series report on the feasibility, safety and outcome
Bibliographic record
Abstract
PURPOSE: Orbital surgery benefits from well-designed instrumentation that offers gentle tissue manipulation, high manoeuvrability and control. Nevertheless, in confined spaces, tissue manipulation must be accomplished with exceptionally high accuracy and precision. This is where robotic surgery offers an advantage. We aimed to evaluate a robotic-assisted surgical system's feasibility, safety and outcome in assisting tumour clearance. PATIENTS AND METHODS: A case series of patients with advanced periocular tumours undergoing robotic-assisted globe-sparing resection was performed using the DaVinci XI system (Intuitive Surgical, Inc). Institutional ethics and multidisciplinary approval were sought in all cases. RESULTS: Four patients with advanced periocular tumours underwent robotic-assisted orbital surgery at a mean age of 63 years (range 42-86). Two patients were diagnosed with squamous cell carcinoma, and two had basal cell carcinoma. One patient was found to have positive lymph nodes at the time of surgery and underwent simultaneous parotidectomy and lymph node clearance. Clear resection of the primary tumour was achieved in all patients; three patients underwent further resection due to narrow margins prior to reconstruction. Patients were follow-up for at least one year, and three remained disease-free. One patient with pre-existing extra-orbital disease developed metastatic disease four months post-op. All patients preserved vision peri-operatively, with no complaints of diplopia. Moderate ocular surface disease was noted in two patients. CONCLUSION: Our series highlights the potential advantage of three-dimensional optics, multi-directional instrumentation and motion scaling technology to achieve globe-sparing tumour resection in advanced periocular tumours. However, further robotic instrumentation development is required for orbital surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".