Fetoscopic Robotic Open Spina bifida Treatment (FROST): feasibility and learning curve study in a preclinical representative training model
Bibliographic record
Abstract
Objective: The primary aim was to assess the feasibility of robotic OSB repair in a preclinical high-fidelity 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 exteriorized uterus for laparotomy-assisted laparoscopic OSB surgery. It included a silicone uterus, placenta, and fetal manikin with a simulated OSB lesion, replicating the lesion sac, paraspinal muscles, and neural placode. Population: Four surgeons participated: an expert MFM consultant (TVM), a neurosurgical consultant (AK), an MFM fellow (novice 1, YK), and a neurosurgical resident (novice 2, CK). Methods: The surgical procedure included 8 steps: uterine access, working space creation, lesion exposition, junctional zone dissection, skin mobilization, dural patch application, and closure of myofascial flaps and skin. Success was defined by precise restoration (suture interval <3mm), total operative time ≤180 minutes, 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 fetal OSB surgery in a high-fidelity simulation is feasible, showing promising outcomes for a large animal model and clinical translation .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".