Malignant collision tumors in two dogs
Bibliographic record
Abstract
CASE DESCRIPTION A 13-year-old Labrador Retriever with a 4-cm-diameter ulcerated perianal mass and a 12-year-old Golden Retriever with a 5-cm-diameter ulcerated caudolateral abdominal mass were brought to a referral oncology practice for evaluation of the dermal masses. Both masses were resected with wide margins without reported postoperative complications. For both dogs, a diagnosis of collision tumor was made. The database of the Veterinary Diagnostic Laboratories at Colorado State University was searched for other examples of collision tumors in dogs. CLINICAL FINDINGS Histologic assessment of the masses revealed collision tumors in both patients. The perianal mass was diagnosed as a perianal gland carcinoma with adjacent hemangiosarcoma. The flank mass was diagnosed as a fibrosarcoma with an adjacent mast cell tumor. The university database search of sample submissions in 2008 through 2014 for the keywords collision, admixed, or adjacent yielded 37 additional cases of dogs with malignant nontesticular collision tumors. TREATMENT AND OUTCOME Both dogs were treated with surgery alone and received no adjunctive treatments. Both tumors were completely excised. There was no evidence of either local tumor recurrence or metastasis in the Labrador Retriever and the Golden Retriever at 1,009 and 433 days after surgery, respectively. CLINICAL RELEVANCE Collision tumors are rare, and there is minimal information regarding treatment recommendations and outcome for animals with collision tumors. On the basis of the 2 cases described in this report, the outcome associated with treatment of collision tumors may be similar to the expected outcome for treatment of any of the individual tumor types in dogs.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".