Multiple cartilaginous exostosis in a Golden Retriever cross-bred puppy
Bibliographic record
Abstract
Multiple cartilaginous exostosis was diagnosed in a six-month-old Golden Retriever cross-bred male with a history of forelimb lameness and isolated, but very painful, acute episodes. Physical examination revealed a right forelimb lameness with a firm, painful palpable mass on the cranial aspect of the forearm. The radiological examination showed the presence of bony masses at the humerus and radius as well as several masses in the ribs and spinous processes of the thoracic vertebrae. Based on the history and radiographic findings, multiple cartilaginous exostosis was diagnosed. Treatment with non-steroidal anti-inflammatory drugs was commenced for two weeks without any effect. Due to the lack of a response to the treatment as well as to the progressive physical deterioration of the animal, the owners requested euthanasia of the dog. Histology of the different exostoses demonstrated the presence of a hyaline cartilage cup surrounding a central area, formed mainly by bone and cartilage trabecullae. Signs of malignancy were not observed. Back-scattered scanning electron microscopy (BEI-SEM) study revealed well ordered and progressively calcified cartilage trabecullae present underneath the non-calcified cartilage cap. At a greater depth, those cartilage trabecullae became osteochondral trabecullae, and the innermost were formed exclusively by woven and lamellar bone. The histological and back-scattered electron scanning microscopy results conclude that it was a well-arranged normal endochondral ossification process that followed a centripetal pattern inside the bony mass, confirming the diagnoses of multiple cartilaginous exostoses.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".