Orofacial masses in domestic rabbits: a retrospective review of 120 cases from 2 institutions, 2000–2023
Bibliographic record
Abstract
Orofacial masses or swellings are a common presenting complaint in lagomorphs. Similar gross appearances of the masses can complicate clinical interpretation, and histologic review often provides the final diagnosis. Underlying causes vary from infectious to neoplastic. Although inflammatory changes are most commonly reported, various neoplasms occur, although the prevalence of specific tumor types is relatively unknown. We reviewed retrospectively 120 cases (87.5% biopsy, 12.5% autopsy) of neoplastic and non-neoplastic orofacial masses received from January 2000-February 2023 at 2 institutions: University of Guelph, Canada (Animal Health Laboratory and Department of Pathobiology), and Finn Pathologists, United Kingdom. All final diagnoses were achieved through histologic assessment. We included masses or mass-like swellings from the oral cavity, including the mandible and maxilla, and surrounding skin and soft tissues of the oral cavity and jaw. Submissions included pet and commercial (meat and fur) rabbits. Neoplastic lesions were most common (60%), including trichoblastomas, papillomas, melanocytic neoplasms, sarcomas, round-cell tumors, carcinomas (including squamous cell carcinoma), lipomas, odontogenic neoplasms, polyps, osteoma, neuroma, peripheral keratinizing ameloblastoma, and apocrine adenoma. Inflammatory diagnoses (30%) included abscesses, osteomyelitis, dermatitis, and sialadenitis. Other diagnoses (7%) included cysts, as well as hyperplastic skin and proliferative bone lesions. Three cases had no definitive diagnosis. The importance of histologic assessment in diagnosing orofacial "masses" in rabbits is highlighted, given that the most common diagnostic category overall was neoplasia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".