Tibial Plateau and Stifle Joint Invasion with a Subcutaneous Mast Cell Tumor
Bibliographic record
Abstract
A 4 yr old female neutered Labrador retriever was referred with a history of left hind-limb lameness and an acute, nonpainful, subcutaneous mass on the medial aspect of the left stifle. Stifle radiographs and fine needle aspirates of the soft tissue mass performed by the referring veterinarian confirmed the presence of predominantly highly granulated mast cells, consistent with a mast cell tumor. Computed tomography demonstrated a soft tissue mass centered on the left medial stifle, with associated joint effusion and polyostotic lytic lesions on the tibial plateau and distal patella. Ultrasound-guided aspirates of the liver, spleen, and popliteal lymph nodes were obtained to rule out further metastatic spread. Cytology of the joint fluid demonstrated a low number of well-differentiated mast cells. Surgical and oncological interventions were discussed, and full hind-limb amputation was elected. Histopathological analysis of the submitted tissues after amputation diagnosed a subcutaneous mast cell tumor with neoplastic cell infiltrate extending into sections of joint capsule and synovial membrane. Infiltration to the tibia and distal patella were suspected following the presence of mast cell clusters in both osteolytic lesions. No evidence of metastasis was identified in the popliteal lymph node. Postoperative monitoring of iliac lymph node size using ultrasound did not identify evidence of metastasis 12 mo postoperatively.
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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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".