A Da Vinci Open Spina Bifida Suturing Simulator with Continuum Tools for Surgeon Skills Training
Bibliographic record
Abstract
Open Spina Bifida (OSB) is a congenital neural tube defect that affects approximately 1 in 1000 births worldwide. Robotic in-utero OSB repair provides a minimally invasive alternative to open-surgery, which places significant strain on both baby and mother. Recent advancements in da Vinci miniature continuum tools reduce port sizes through the uterus for access to the fetus with lower maternal risk. However, idiosyncrasies in continuum tool behaviour further complicate an already difficult procedure. Consequently, a high-fidelity da Vinci OSB repair simulator is presented featuring continuum tools for surgeon skills training. The simulator incorporates a plugin for suture physics handling, soft body physics for deformable tissues and implements haptic virtual fixtures for improved situational awareness during suturing. Quantitative validation demonstrated virtual tool accuracy, with a mean-squared continuum backbone error of 0.64 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and system-level end-effector trajectory errors averaging 3.25 mm for a helix tracing task. During suturing, high-fidelity performance was maintained. Four expert surgeons from relevant specialties provided positive qualitative feedback, reporting that the simulator accurately replicates real tool control and offers a realistic and valuable training experience. Ultimately, the simulator shows promise as a training platform for safer robotic in-utero OSB repair and facilitating the adoption of novel continuum wristed tools in clinical settings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".