Computed Tomography Findings of Pigmented Villonodular Synovitis in a Dog
Bibliographic record
Abstract
Pigmented villonodular synovitis (PVNS) is a rare benign and usually monoarticular neoplastic lesion arising from the synovium, bursae and tendon sheaths in humans, horses and dogs. Categorization for PVNS in humans includes localized and diffuse forms of PVNS and tenosynovial giant cell tumour (TGCT), although histologically they are the same. The localized form is characterized by discrete nodular lesions, the diffuse form is often intra-articular, infiltrative, affecting the entire synovium with more aggressive behaviour and TGCT occurs along tendon sheaths. Computed tomography (CT) of PVNS is well described in humans but not documented in the veterinary literature. Pigmented villonodular synovitis is not a straightforward diagnosis and CT is useful to further characterize radiographic findings. A representative open surgical biopsy of the synovium is essential to obtaining the diagnosis and ruling out malignancy. Currently, there are no guidelines for the diagnosis of PVNS in dogs or long-term follow-up of these cases. This case report describes the presentation, diagnostic findings, treatment and long-term outcome of a 4-year-old male Labrador Retriever with confirmed PVNS. Clinical outcome was considered fair with the dog's lameness and symptoms remaining stable with medical management 3 years following the initial diagnosis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".