Canine Uterine Leiomyoma with Epithelial Tissue Foci, Adenomyosis, and Cystic Endometrial Hyperplasia
Bibliographic record
Abstract
An 11-year-old Labrador Retriever bitch with a history of intermittent, sanguineous vaginal discharge of a six-month duration was presented. During exploratory laparotomy, two well-delineated, intramural masses were identified bilaterally in the uterine horns. Histopathologic examination of the mass on the left horn showed that it was a typical leiomyoma. However, the second mass appeared with an unusual coexistence of histological lesions, involving epithelial tissue foci, mild focal adenomyosis, and cystic endometrial hyperplasia. Interestingly, such combination was never encountered before in dogs. Although uterine leiomyoma is quite usual in the reproductive system of female dogs, this case resembled relevant cases of human uterine adenomyomas in morphology, and thus it was offered a similar tentative diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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".