Clinical, computed tomographic, magnetic resonance imaging, and histologic findings associated with myxomatous neoplasia of the temporomandibular joint in two dogs
Bibliographic record
Abstract
CASE DESCRIPTION A 15-year-old neutered female mixed-breed dog (dog 1) and an 11-year-old neutered female Labrador Retriever (dog 2) were examined because of unilateral exophthalmus, third eyelid protrusion, and periorbital swelling that failed to respond to antimicrobial treatment. CLINICAL FINDINGS Both dogs underwent ultrasonographic, CT, and MRI examination of the head. In both dogs, advanced imaging revealed a poorly defined, peripherally contrast-enhancing, mucous-filled cystic mass that radiated from the temporomandibular joint and infiltrated the periorbital tissues and retrobulbar space. Both dogs underwent surgical biopsy of the periorbital mass. A viscous, straw-colored fluid was aspirated from the retrobulbar region in both dogs. The initial histologic diagnosis for dog 1 was zygomatic sialadenitis and sialocele. However, the clinical signs recurred, and histologic examination of specimens obtained during a second surgical biopsy resulted in a diagnosis of myxoma. The histologic diagnosis was myxosarcoma for dog 2. TREATMENT AND OUTCOME In both dogs, clinical signs recurred within 2 weeks after surgery and persisted for the duration of their lives. Dog 1 received no further treatment after the second surgery and was euthanized 34 months after initial examination because of multicentric lymphoma. Dog 2 was treated with various chemotherapy agents and was euthanized 11 months after initial examination because of a dramatic increase in periocular swelling and respiratory stertor. CLINICAL RELEVANCE Temporomandibular myxomatous neoplasia can be confused with zygomatic sialocele on the basis of clinical signs but has characteristic MRI features. Representative biopsy specimens should be obtained from areas close to the temporomandibular joint to avoid misdiagnosis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".