Cryoglobulinemia Associated With Multiple Myeloma in a Dog Presenting With Epistaxis and Skin Lesions
Bibliographic record
Abstract
A 10-year-old female neutered Labrador Retriever presented with epistaxis, discoloration and crusting of the nose and a necrotic lesion on the lip. Bloodwork revealed pancytopenia, azotemia, hypoalbuminemia and hyperglobulinemia. Aggregates of amorphous basophilic material were seen in a room-temperature blood smear which were not present in the sample after warming to 37°C, and grossly a cryoprecipitate was noted in the patient's serum at 4°C. This was interpreted as cryoglobulin. Computed tomography showed multiple heterogeneous lesions in the spleen. Cytology of the splenic lesions revealed marked plasma cell infiltration, consistent with neoplasia. Bone marrow aspiration revealed an increased proportion of plasma cells (approximately 38% of the total cells). Serum protein electrophoresis showed a monoclonal spike in the gamma globulin region. A diagnosis of multiple myeloma associated with cryoglobulinemia was made. The patient received palliative care with prednisolone while the owner was considering chemotherapy. However, she rapidly deteriorated and was euthanized. The combination of cryoglobulin precipitation and hyperviscosity syndrome was considered responsible for the patient's original symptoms. Cryoglobulinemia is an extremely rare phenomenon that is often associated with lymphoproliferative disorders. This report describes its association with multiple myeloma in a dog presenting with atypical initial signs.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".