Diagnosis and Classification of Primary Nodal Lymphomas in Dogs: A Consensus of the Oncology‐Pathology Working Group
Bibliographic record
Abstract
One of the primary objectives of the Oncology Pathology Working Group (OPWG) is for oncologists and pathologists to collaboratively generate consensus documents to standardise aspects of and provide guidelines for oncologic pathology in veterinary species. Consensus is established through critical review of the peer-reviewed literature relevant to a subgroup's particular focus. In this article, the authors provide a critical review of the current literature regarding methods for the diagnosis and classification of primary nodal lymphomas of dogs, including histopathology, cytopathology, immunophenotyping and assessment of molecular clonality. Knowledge gaps in the current literature and recommendations for future study are also reported. Major conclusions of this consensus include: (1) Histopathology with immunohistochemistry is required for complete diagnosis and classification of nodal lymphomas; (2) Immunohistochemistry and flow cytometry are the most reliable methods of immunophenotyping lymphomas, though neither is clearly superior to the other; (3) Molecular clonality testing should not be used in favour of immunophenotyping assays for classifying lymphomas; and (4) The use of emerging molecular tests for diagnosing lymphomas in the absence of histopathologic, cytopathologic, or immunophenotypic disease characterisation should be restricted to investigational settings until their diagnostic validity and the clinical benefit they confer to patients are more thoroughly characterised. This document represents the opinions of the OPWG and the authors; it does not constitute a formal endorsement by the American College of Veterinary Pathologists or the Veterinary Cancer Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".