In utero Repair of an Experimental Neural Tube Defect in a Chronic Sheep Model Using Biomatrices
Bibliographic record
Abstract
OBJECTIVE: Persistent exposure of the unprotected spinal cord to amniotic fluid and the uterine wall can lead to progressive damage of neural tissue in case of a myelomeningocele (two-hit hypothesis). The aim of this study was to evaluate whether in utero repair of an experimental neural tube defect in a fetal lamb could protect neural tissue from secondary injury and save neurologic functions after birth. METHODS: In 19 fetal lambs, a neural tube defect was created at 79 days' gestation. In 12 lambs the defect was covered either with a novel, molecular defined collagen-based biocompatible and biodegradable matrix (UMC) or with a small intestinal submucosa (SIS) biomatrix (Cook) or by closing the skin over the defect. RESULTS: All lambs with the defect covered showed no or minor neurologic morbidity in contrast to the lambs with the defect uncovered in which major neurologic morbidity was seen. CONCLUSIONS: These results demonstrate that long-term exposure of the open spinal cord to the intrauterine environment can lead to damage of neural tissue and, consequently loss of neurologic functions and that coverage of the defect can lead to a better neurologic outcome. Furthermore, we could show that a UMC biomatrix and an SIS biomatrix are useful for in utero coverage of a surgically created neural tube defect in our model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".