Determining the best graft‐sealant combination for skull base repair using a soft tissue in vitro porcine model
Bibliographic record
Abstract
BACKGROUND: The purpose of this work was to compare the absolute breaking strength of various soft tissue skull base (SB) repairs in an in vitro porcine model. METHODS: A burst pressure (BP) testing system was designed using an axial loading force to create increasing hydraulic pressure. Defects measuring 0.5 × 0.5 cm were created in fascia lata samples. Defects were repaired using various grafts (pericranium and 2 different dural substitutes, Alloderm(®) and Durasis(®)) measuring 1.0 × 1.0 cm to cover the deficient area. Grafts were further reinforced onto the fascia background with either fibrin glue (Tisseel(®)) or hydrogel sealant (DuraSeal™). Each combination of graft and sealant was conducted 6 times and tested 24 hours after the repair. RESULTS: The mean BP (±standard deviation [SD]) were as follows: DuraSeal™-Alloderm, 12.5 ± 5.8 mmHg; DuraSeal™-Durasis, 21.8 ± 20.7 mmHg; DuraSeal™-pericranium, 44.7 ± 30.1 mmHg; Tisseel-Alloderm, 30.6 ± 26.3 mmHg; Tisseel-Durasis, 15.8 ± 18.6 mmHg; and Tisseel-pericranium, 95.5 ± 86 mmHg. One-way analysis of variance showed that the strongest type of repair was Tisseel-pericranium when adjusting for the others (p < 0.0001). The difference in mean BP of repair with DuraSeal™ vs Tisseel(®) was not statistically significant (p = 0.22). Comparing sealants, the use of Alloderm(®) or Durasis(®) decreased the strength of the repair in comparison to pericranium (p < 0.0001). Bonferroni analysis showed a significant difference between pericranium and Alloderm(®) (p < 0.05) and between pericranium and Durasis(®) (p < 0.05) but not between Alloderm(®) and Durasis(®) (p > 0.05). CONCLUSION: In this model, the strongest type of repair (pressure 6 times higher than normal intracranial pressure) was the combination of Tisseel(®)-pericranium. Our data will help guide surgeons who repair SB defects to choose the best graft and sealant.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".