Pathological and immunohistochemical characterization of naturally occurring lymphoma in golden retriever dogs
Bibliographic record
Abstract
Leading veterinary oncologists consistently identify the Golden Retriever as the breed most predisposed to lymphoma, with lifetime incidence estimates approaching 1 in 8 individuals, yet the immunophenotypic and histological profile of lymphoma in this breed has been incompletely characterised in South American populations. This research examined 35 naturally occurring lymphoma cases in Golden Retrievers diagnosed at the Santiago Institute of Veterinary Pathology, Chile, between January 2020 and December 2023. All cases underwent standardised histopathological evaluation with haematoxylin and eosin staining, WHO classification, and an immunohistochemical (IHC) panel comprising CD3, CD79a, PAX5, Ki-67, Bcl-2, and MUM1. B-cell lymphoma predominated (51.4%; 18/35), followed by T-cell lymphoma (48.6%; 17/35). The most common WHO subtype was diffuse large B-cell lymphoma (DLBCL; 42.9%), followed by peripheral T-cell lymphoma not otherwise specified (PTCL-NOS; 31.4%). Ki-67 proliferative index exceeded 50% in 72.2% of B-cell cases and 58.8% of T-cell cases. Bcl-2 positivity was more frequent in B-cell tumours (66.7% versus 29.4%; p = 0.019). MUM1 expression, an indicator of post-germinal-centre origin, was detected in 44.4% of B-cell cases. Median age at diagnosis was 8.7 years, and multicentric presentation accounted for 74.3% of cases. These findings provide a comprehensive immunohistochemical reference profile for Golden Retriever lymphoma in a Chilean population and highlight the predominance of high-grade, proliferative subtypes that may benefit from aggressive combination chemotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".