Comparison of two laparoscopic treatments for experimentally induced abdominal adhesions in pony foals
Bibliographic record
Abstract
OBJECTIVE: To compare laparoscopic dissection with-laparoscopic dissection combined with abdominal instillation of ferric hyaluronate gel for the treatment of experimentally induced adhesions in pony foals. ANIMALS: 12 healthy pony foals. PROCEDURE: A serosal abrasion method was used to create adhesions at 4 sites on the jejunum (day 0). At day 7 laparoscopy was performed and the adhesions observed in each foal were recorded. In group-1 foals (n = 6), the adhesions were separated laparoscopically (treatment 1). In group-2 foals (n = 6), 300 mL of 0.5% ferric hyaluronate gel was infused into the abdomen after the adhesions were separated laparoscopically (treatment 2). At day 24, terminal laparoscopy was performed and the adhesions observed were recorded. Total number of adhesions within each group was compared between day 7 and 24. Data were analyzed to determine whether an association existed between the number of adhesions on day 24 and treatment type. RESULTS: At day 24, the number of adhesions was significantly decreased within each group, compared with the number of adhesions at day 7 (group-1 foals, 10 vs 22 adhesions; group-2 foals, 3 vs 20 adhesions). Treatment 1 was associated with a significantly higher number of adhesions at day 24, compared with treatment 2 (odds ratio, 4.54; 95% confidence interval, 1.03 to 23.02). CONCLUSION AND CLINICAL RELEVANCE: Abdominal instillation of 0.5% ferric hyaluronate gel after laparoscopic dissection was a more effective technique than laparoscopic dissection alone to treat experimentally induced adhesions in pony foals. Laparoscopic adhesiolysis following abdominal surgery in foals is a safe and effective technique.
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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 | 0.002 |
| 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 | 0.001 |
| 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".