Efficacy of Fibrin Glue and Polyglactin Acid Sheet for Pig Liver Resection Model
Bibliographic record
Abstract
Background: Fibrin glue is commonly used to prevent bile leakage and bleeding after hepatectomy in clinical cases. Recently, several liver centers try to use polyglactin acid sheet for preventing bile leakage and bleeding in Japan. However, there is no evidence of its efficacy and safety when applied to cut liver surface after surgery. Therefore we evaluated the efficacy of fibrin glue and polyglactin acid sheet using pig liver resection model. Methods: A chevron incision was performed under general anesthesia, followed by the left hemi-hepatectomy (approximately 40%), using pig liver. Pigs were subsequently allocated randomly into 2 groups (n = 5 in both); in group A, a fibrin glue (Bolheal) with polyglactin acid sheet (Neoveil) was applied to the cut surface and in group B only Bolheal was applied. After one month, we evaluated histological findings and incidence of biloma or inflammatory change at the cut surface. Results: All of the 5 pigs in group A had fibrotic capsulated cavity at the cut surface. Inside of those cavities included necrotic liver tissues, Bolheal, and peace of Neoveil with bile juice. Histologically, Neoveil and Bolheal remained in the fibrotic tissue in normal liver tissue. In group B, only one pig had abscess at cut surface. Other four pigs showed no histological problems; Bolheal was completely integrated into the normal liver parenchyma. Conclusion: Bolheal was very effective for liver cut surface in this experimental study. Neoveil should not be applied for cut surface. doi:10.4021/jcs54w
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".