Global variability in fetal spina bifida surgery: a survey of neurosurgical strategies
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the global variability in intraoperative neurosurgical management strategies for fetal spina bifida surgery. METHODS: All prenatal fetal spina bifida surgery centers identified through the International Society of Prenatal Diagnosis website and previous literature were invited to participate in an online survey addressing various aspects of the surgery, including fetal selection criteria, surgical technique, and common intraoperative challenges. RESULTS: Thirty-four centers (72%) responded to the survey, more than half of whom perform fewer than 10 surgeries annually (56%). The most common earliest gestational age (GA) for fetal surgery was 23 (36%, n = 12/33), ranging from < 21 weeks (9%, n = 3) to > 24 weeks (9%, n = 3). The latest GA for surgery varied from < 26 weeks (24%, n = 8) to 30 weeks (3%, n = 1), with the majority setting a cutoff at 26 weeks (50%, n = 17). Open fetal surgery is the predominant method in 76% of centers (n = 26), followed by a hybrid approach (laparotomy with fetoscopy on the uterus; 29%, n = 10) and fully percutaneous fetoscopic surgery (15%, n = 5). Filum terminale dissection is performed in 58% (n = 19/33) of centers and placode tubularization in 46% (n = 15/33). Myofascial flaps are routinely used in 55% of the centers (n = 18/33). When primary skin closure is not possible, 39% (n = 13/33) will use releasing side cuts and one-third of all centers will use acellular dermal matrix grafts (33.3%, n = 11/33). Extensive skin defects and suboptimal fetal access were commonly cited as the most significant intraoperative challenges. CONCLUSIONS: There is variability in the fetal inclusion criteria and intraoperative management of fetal spina bifida across centers. This variability emphasizes the need for more research on best practices as well as standardized outcome reporting (ideally through "core outcomes") to allow for comparison between centers. Identified challenges, such as difficulties in skin closure, highlight specific areas for future innovations in the field.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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".