Quality of Adhesions After Sutured Paramedian vs. Laparoscopic Toggle Abomasopexy in an Ovine Model
Bibliographic record
Abstract
OBJECTIVE: To evaluate adhesions created by abomasopexies using either chromic gut or polydioxanone suture through a right paramedian approach and determine whether a laparoscopic toggle technique is an acceptable alternative to open abomasopexy using an ovine model. STUDY DESIGN: Experimental study. ANIMALS: Mature ewes (n=30). METHODS: Ewes received 1 of 3 abomasopexy techniques (n=10): right paramedian approach using chromic gut or polydioxanone, or a laparoscopic toggle technique. After euthanasia 8 weeks postoperatively, adhesions were removed en bloc and adhesion cross-sectional area (width × length) and depth (distance from abdominal wall to abomasum) were measured and given a grade of 0-3 based on the quality of adhesion. Surgical time was recorded and compared for each technique. Significance was set at P≤.05. RESULTS: Abomasopexies performed with either suture material resulted in a significantly larger mean cross-sectional area and higher adhesion grades compared to those performed using the toggle. Width and length of adhesions formed using chromic gut or polydioxanone were not significantly different; however, both were significantly wider and longer than those formed using the toggle. The laparoscopic toggle technique required significantly less surgical time than the sutured techniques. CONCLUSION: Polydioxanone is as effective as chromic gut suture material in inducing abomasal adhesion formation in our sheep model. The clinical significance of the size and grade of adhesions formed is unknown and requires further investigation before the laparoscopic toggle technique can be recommended as a replacement for paramedian abomasopexy in cattle for the treatment of displaced abomasum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".