Osteosarcoma masked by osteomyelitis and cellulitis in a dog
Bibliographic record
Abstract
CASE DESCRIPTION: This clinical report describes a 10-year-old female spayed German Shepherd dog cross that was presented with cellulitis of the left proximal forelimb and osteomyelitis of the left proximal humerus, and was ultimately diagnosed with metastatic osteosarcoma. CLINICAL FINDINGS: The diagnosis of cellulitis and osteomyelitis was made using ultrasound, radiography, cytology and histopathology, all of which were consistent with cellulitis and osteomyelitis. Cultures were negative. TREATMENT: The patient was treated using two surgical debridements and long-term broad-spectrum antibiotic drugs. Despite surgical and medical treatment, the dog's condition progressed. A lytic lesion of the left proximal humerus was identified radiographically. OUTCOME: One hundred forty-one days after initial presentation, the dog was presented with a non-weight bearing lameness of the left forelimb. An amputation was scheduled. Preoperative computed tomography scan of the thorax revealed gross metastatic disease to the lungs. The patient was euthanatized and a post-mortem examination revealed osteosarcoma of the left proximal humerus with widespread metastasis. CLINICAL RELEVANCE: To our knowledge, this case is the first reported case of osteomyelitis masking osteosarcoma in a dog. It serves as a reminder to maintain a high index of suspicion when managing cases with a signalment, history and radiographic lesion that are consistent with a primary bone tumour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".