Fetoscopic Robotic Open Spina Bifida Treatment (<scp>FROST</scp>): A Preclinical Feasibility and Learning Curve Study
Bibliographic record
Abstract
OBJECTIVE: The primary aim was to assess the feasibility of robotic OSB repair in a simulation training model, documenting the learning curve and ensuring quality control among surgeons. DESIGN: The learning curve was assessed using the cumulative summation test (LC-CUSUM). Following LC-CUSUM, six additional experiments were performed for competency-cumulative summation (C-CUSUM) analysis to ensure ongoing quality control. SETTING: The simulator was created through 3D printing and hand sculpting, simulating a partially exteriorised uterus for laparotomy-assisted laparoscopic OSB surgery. It included a silicone uterus, placenta and foetal manikin with a simulated OSB lesion, replicating the lesion sac, paraspinal muscles and neural placode. POPULATION: Four surgeons participated: an expert Maternal Fetal Medicine consultant, a neurosurgical consultant, a Maternal Fetal Medicine fellow and a neurosurgical resident. METHODS: The surgical procedure included eight steps: uterine access, working space creation, lesion exposition, junctional zone dissection, skin mobilisation, dural patch application, closure of myofascial flaps and closure of skin. Success was defined by precise restoration (suture interval < 3 mm), foetal repair time ≤ 120 min and a GEARS score > 21/30. MAIN OUTCOMES: Learning curve and competency were documented via LC-CUSUM and C-CUSUM. RESULTS: Competence was achieved after 15-21 procedures, with novices reaching competency within this range. Participants maintained high performance in subsequent quality-controlled procedures. CONCLUSION: Robotic-assisted foetal OSB surgery in a high-fidelity simulation is feasible, showing promising outcomes for a large animal model and clinical translation.
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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 | 0.002 |
| 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 | 0.000 | 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".