Development of a domestic animal model for endometriosis: Surgical induction in the dog, pigs, and sheep
Bibliographic record
Abstract
Background: Endometriosis affects one in ten women of reproductive age but it is diagnosed at advanced stages. Our objective was to develop a domestic animal model that would permit sequential assessment of endometriotic changes. Materials and methods: Surgical transplantation of endometrial tissue and fat (n = 4 grafts/tissue/animal) was done in dogs (n = 5), pigs (n = 4), and sheep (n = 5). Autologous grafts were sutured to the visceral (urinary bladder in dogs and pigs and uterus in sheep) and parietal peritoneum. Sham surgeries were performed (dogs and sheep n = 5 and pigs n = 3) by placing fat grafts alone. Plasma estrogen and progesterone concentration was performed prior to surgery and weekly following surgery until euthanasia. Animals were euthanized between 80 and 110 days after surgery. Gross and histopathologic features of endometriotic lesions were recorded. Results: A variety of lesions from transplanted endometrial grafts included endometriotic cysts, vesicles, solid lesions, or absence of lesions. The proportion of cysts was greater (p < 0.01) in dogs (18/20 grafts) than in pigs (5/16) and sheep (5/20). The area of endometriotic lesions at the time of euthanasia was greater than at the time of surgery in dogs (0.89 ± 0.11 cm 2 vs 0.50 ± 0.09 cm 2 ; p < 0.05), whereas, the size of lesions decreased (p < 0.05) by half or more in pigs and sheep. In dogs, endometrial cysts were characterized by simple cuboidal/columnar epithelium, endometrial glands, stromal tissue with hemorrhage and/or hemosiderin-laden macrophages, and smooth muscle cells. Conclusion: The development of endometriotic cysts was apparent in dogs than in sheep and pigs. Therefore, dog is a better domestic animal model for endometriosis.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".