A retrospective study of lingual lesions in 793 dogs and 406 cats at the Athens Veterinary Diagnostic Laboratory, 2010–2020
Bibliographic record
Abstract
Lingual biopsies are a common type of sample submission at the Athens Veterinary Diagnostic Laboratory (AVDL). Here we describe the pathology diagnoses of 793 canine and 406 feline lingual biopsies submitted to the AVDL in a 10-y period. Non-neoplastic lesions accounted for 450 diagnoses (57%) in dogs and 239 diagnoses (59%) in cats. Canine non-neoplastic lesions consisted of inflammatory lesions (286 cases; 64% of non-neoplastic lesions) and tumor-like proliferative lesions (164 cases; 36% of non-neoplastic lesions). Feline non-neoplastic lesions consisted of inflammatory lesions (228 cases; 95% of non-neoplastic lesions) and tumor-like proliferative lesions (11 cases; 5% of non-neoplastic lesions). The most common canine neoplasms were melanocytic neoplasms (103 cases; 30% of neoplasms) and epithelial neoplasms (102 cases; 30% of neoplasms), followed by mesenchymal neoplasms (90 cases; 26% of neoplasms) and round cell neoplasms (48 cases; 14% of neoplasms). Approximately 43% of melanocytic neoplasms affected Chow Chows and Labrador Retrievers, and 20% of epithelial neoplasms affected Labrador Retrievers. In cats, most tumors were epithelial (158 cases; 94% of neoplasms), followed by mesenchymal (8 cases; 5% of neoplasms) and round cell neoplasms (1 case; 1% of neoplasms). Over 50% of neoplasms of cats affected domestic shorthair cats. Although the percentage of lingual biopsies that had a neoplastic diagnosis was roughly the same between species, the diversity of neoplasms was much greater in dogs than in cats.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".