Cervico-Vaginal Multiple Leiomyomas in a Labrador Dog: A Case Report
Bibliographic record
Abstract
Leiomyomas are common in the canine female reproductive tract and accounts for 2.4% of all canine neoplasms (Hulland, 1978). Primary vaginal tumors are rare. They are usually secondary to either cervical or vulval lesions. In the vagina, leiomyoma usually presents as a solid single nodule mostly, firm consistency, brown color. In bitches, the tumor appears at middle and old age. Preoperative diagnosis of leiomyoma is challenging. Therefore leiomyoma is often diagnosed during postoperative histologic evaluation. Despite this, there are very few reports of them, apart from occasional surgical or clinical articles. The aim of this paper is to report a case of cervico-vaginal multiple leiomyoma in Labrador bitch.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".