Classification of Canine Malignant Lymphomas According to the World Health Organization Criteria
Bibliographic record
Abstract
A study was carried out to test the accuracy and consistency of veterinary pathologists, not specialists in hematopathology, in applying the World Health Organization (WHO) system of classification of canine lymphomas. This study represents an initiative of the ACVP Oncology Committee, and the classification has been endorsed by the World Small Animal Veterinary Association (WASVA). Tissue biopsies from cases of canine lymphoma were received from veterinary oncologists, and a study by pathologists given only signalment was carried out on 300 cases. Twenty pathologists reviewed these 300 cases with each required to choose a diagnosis from a list of 43 B and T cell lymphomas. Three of the 20 were hematopathologists who determined the consensus diagnosis for each case. The 17 who formed the test group were experienced but not specialists in hematopathology, and most were diplomates of the American or European Colleges of Veterinary Pathology. The overall accuracy of the 17 pathologists on the 300 cases was 83%. When the analysis was limited to the 6 most common diagnoses, containing 80% of all cases, accuracy rose to 87%. In a test of reproducibility enabled by reintroducing 5% of cases entered under a different identity, the overall agreement between the first and second diagnosis ranged from 40 to 87%. The statistical review included 43,000 data points for each of the 20 pathologists.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".