A comparison of the application of fibrin glue and adhesive film for repair of anastomotic leaks in the rat
Bibliographic record
Abstract
BACKGROUND: Anastomotic leaks constitute one of the most serious intraoperative complications and although many studies have been devoted to finding a solution for this problem, none of them has yet been able offer a decisive, successful method. In this study, the ability of fibrin glue and adhesive film to repair anastomotic leaks in an experimental model was compared. MATERIALS AND METHODS: The sample comprised four groups of seven rats: Group 1 (Control): the distal colon was transected and anastomosis was performed. Group 2 (Primary repair): incomplete anastomosis produced a leak that was closed by primary repair on day 3. Group 3 (Fibrin glue): incomplete anastomosis produced a leak that was closed by primary repair and fibrin glue applied on day 3. Group 4 (Adhesive film): incomplete anastomosis produced a leak that was closed by primary repair and adhesive film was applied on day 3. The rats were sacrificed on day 6 following anastomosis. Anastomotic blast compressions were measured and fibroblast activation, inflammation, neovascularization and levels of collagen were evaluated. RESULTS: The results from Group 4 showed that blast compression values were high and statistically significantly increased over control values (p < 0.05). Inflammation in Group 2 was significantly higher than the other groups (p < 0.05). No significant differences were detected in the comparison of the groups regarding the other scoring criteria (p > 0.05). CONCLUSION: Adhesive film is more effective in reducing anastomotic leakage than fibrin glue.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".