Frequency of and risk factors associated with lingual lesions in dogs: 1,196 cases (1995–2004)
Bibliographic record
Abstract
OBJECTIVE: To categorize histologic lesions affecting the tongue, determine the frequency with which they develop, and identify risk factors associated with their development in dogs. DESIGN: Retrospective case series. ANIMALS: 1,196 dogs. PROCEDURES: Diagnostic reports of lingual biopsy specimens from dogs evaluated from January 1995 to October 2004 were reviewed. RESULTS: Neoplasia comprised 54% of lingual lesions. Malignant tumors accounted for 64% of lingual neoplasms and included melanoma, squamous cell carcinoma, hemangiosarcoma, and fibrosarcoma. Large-breed dogs, especially Chow Chows and Chinese Shar-Peis, were at increased risk for melanoma. Females of all breeds and Poodles, Labrador Retrievers, and Samoyeds were more likely to have squamous cell carcinomas. Hemangiosarcomas and fibrosarcomas were commonly diagnosed in Border Collies and Golden Retrievers, respectively. Benign neoplasms included squamous papilloma, plasma cell tumor, and granular cell tumor. Small-breed dogs, especially Cocker Spaniels, were at increased risk for plasma cell tumors. Glossitis accounted for 33% of diagnoses; in most cases, the inciting cause was not apparent. Whereas large-breed dogs were more likely to have lingual neoplasia, small-breed dogs were more likely to have glossitis. Calcinosis circumscripta accounted for 4% of lingual lesions and predominately affected young large-breed dogs. The remaining submissions consisted mostly of various degenerative or wound-associated lesions. CONCLUSIONS AND CLINICAL RELEVANCE: The frequency of lingual lesions was not evenly distributed across breeds, sexes, or size classes of dogs. Veterinarians should be aware of the commonly reported lingual lesions in dogs so that prompt diagnosis and appropriate management can be initiated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".