Safety and efficacy of fetal surgery techniques to close a spina bifida defect in the fetal lamb model: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To determine the safety and efficacy of different neurosurgical techniques for closure of spina bifida (SB) in the fetal lamb model. METHOD: Systematic review of studies reporting on fetal lambs undergoing induction and closure of SB compared with non-operated normal lambs (negative controls) and/or lambs not undergoing closure of the defect (positive controls). Primary outcomes were (1) survival at birth (safety) and/or (2) presence of Somatosensory Evoked Potentials on hind limbs and/or improvement in quantitative histological spinal cord findings and/or reversal of hindbrain herniation (efficacy). RESULTS: Out of 1311, 36 full-text articles were eligible. Nineteen were included for quality assessment. Due to high bias, only 2 adequately powered studies were included in the final analysis. An open approach using a 2-layer closure (muscle flap or acellular-dermal-matrix patch plus skin) was the only safe (patch + skin) and effective (both techniques) technique for prenatal closure in this animal model. No comparable level of evidence was identified for other techniques. CONCLUSION: The experimental literature on prenatal SB closure underscores the lack of standardization. At present, there is animal experimental evidence that a 2-layer closure by hysterotomy is safe and effective. This technique is currently clinically used in a subset of patients. As new clinical techniques are introduced, it would seem logic to preclinically validate them against this experimental standard.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".