Porcine small intestinal submucosa biomaterial graft for repair of a congenital pleuroperitoneal hernia in a kitten.
Bibliographic record
Abstract
In this case, porcine small intestinal submucosa (pSIS) was used to repair a large congenital diaphragmatic hernia in a kitten. The pSIS was moistened in saline, folded in half, and sutured to the remnant of the right hemidiaphragm. The animal was determined to be doing well clinically and radiographically 12 wk after procedure. At 8.5 mo of age, the kitten was spayed, and the diaphragm was inspected, revealing a thin, semitransparent membrane at the central region of the previous pSIS graft. Serial thoracic radiographs may be an effective way to reach a diagnosis of a diaphragmatic hernia if not clearly identifiable on initial radiographs. Surgeons should be prepared to use alternative techniques to close large diaphragmatic defects. Porcine SIS was demonstrated to be a strong, easy-to-use, readily available, and effective technique to close a large defect in the diaphragm with excellent results in the short and medium terms. When hernia repair is employed in juvenile animals, reassessment of the diaphragmatic repair should be considered if future abdominal surgeries, such as ovariohysterectomies, are necessary. A functional 8-ply pSIS should be considered in cats and dogs as it has a lower complication and graft failure rate. Key clinical message: When serial radiographs fail to diagnose a diaphragmatic hernia when one is highly suspected, other modalities, such as ultrasonography or other contrast modalities, should be considered. A functional 8-ply pSIS should be considered to reconstruct the diaphragm, particularly in growing animals, as it is an effective graft with low complication and graft failure rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".