Concordance between clinical presentation and histopathological staging canine mammary tumors
Bibliographic record
Abstract
In this work it was examined the concordance between clinical staging and histopathological staging of mammary tumors in 32 female dogs. It was observed that the average age of presentation of the pathology was 9 years (ranged from 6 to 12 years). The most affected mammary glands were the caudal abdominal and the inguinal, 20 out of 32 female dogs had multiple tumors (62%), and 38% single tumors. Regarding the breeds, the most frequent ones were mixed breeds, Poodle, Cimarron (native breed of Uruguay) and Labrador Retriever. Of the 32 female dogs with breast tumors studied, 65% had histopathological diagnosis of malignant tumor, while 35% had benign tumors. Clinical staging data showed that 64% of the cases with benign tumors were in stage I (1 to 3 cm) and 36% were in stage II (3 to 5 cm). Among those diagnosed with malignant tumors, 10% were in stage V, 57% in stage III, 9% in stage II, and 24% in stage I. There were no animals in stage IV. The most frequently found malignant tumors were tubular carcinoma and complex carcinoma, followed by solid and tubulopapillary carcinomas. Within the benign tumors, complex adenoma was the most frequent, followed by benign mixed tumor and simple ductal papilloma. The concordance between clinical staging and histopathology was low, as we could observe both benign T2 (3 to 5 cm) and malignant T1 (1 to 3 cm) tumors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".