Canine Digital Tumors: A Veterinary Cooperative Oncology Group Retrospective Study of 64 Dogs
Bibliographic record
Abstract
Abstract We compared clinical characteristics and outcomes for dogs with various digital tumors. Medical records and histology specimens of affected dogs from 9 veterinary institutions were reviewed. Risk factors examined included age, weight, sex, tumor site (hindlimb or forelimb), local tumor (T) stage, metastases, tumor type, and treatment modality. The Kaplan-Meier product limit method was used to determine the effect of postulated risk factors on local disease-free interval (LDFI), metastasis-free interval (MFI), and survival time (ST). Outcomes were thought to differ significantly between groups when P± .003. Sixty-four dogs were included. Squamous cell carcinoma (SCC) accounted for 33 (51.6%) of the tumors. Three dogs presented with or developed multiple digital SCC. Other diagnoses included malignant melanoma (MM) (n = 10; 15.6%), osteosarcoma (OSA) (n = 4; 6.3%), hemangiopericytoma (n = 3; 4.7%), benign soft tissue tumors (n = 5; 7.8%), and malignant soft tissue tumors (n = 9; 14%). Fourteen dogs with malignancies had black hair coats, including 5 of the 10 dogs with MM. Surgery was the most common treatment and, regardless of the procedure, had a positive impact on survival. None of the patient variables assessed, including age, sex, tumor type, site, and stage, had a significant impact on ST. Both LDFI and MFI were negatively affected by higher T stage, but not by type of malignancy. Although metastasis at diagnosis correlated with a shorter LDFI, it did not have a significant impact on ST On the basis of these findings, early surgical intervention is advised for the treatment of dogs with digital tumors, regardless of tumor type or the presence of metastatic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".