Unclassified sarcomas: a study to improve classification in a cohort of Golden Retriever dogs
Bibliographic record
Abstract
Morphologically, canine soft-tissue sarcomas (STSs) resemble human STSs. In humans, proper classification of STSs is considered essential to improve insight in the biology of these tumors, and to optimize diagnosis and therapy. To date, there is a paucity of data published on the significance of detailed classification of STSs in the dog. We revised a cohort (n = 110) of proliferative lesions obtained from a study in Golden Retrievers that were considered "soft tissue sarcoma, not otherwise specified or of uncertain subtype" in order to optimize the diagnoses of these lesions. The criteria according to the veterinary WHO classification, recent veterinary literature, and the WHO classification for humans were applied. Revision was initially based on morphologic characteristics of hematoxylin and eosin-stained histologic sections of the neoplasms. If considered necessary (n = 76), additional immunohistochemistry was applied to aid characterization. The diagnosis of STS was confirmed in 75 neoplasms (68%). Of this group, diagnosis of a specific subtype of the STSs was possible in 58 neoplasms. Seven neoplasms had morphologic characteristics that were suggestive for sarcoma subtypes only described in the WHO classification for humans. Seventeen neoplasms remained "unclassified STSs." Thirty-one lesions (28%) were diagnosed "neoplasm, not being STS." Four lesions (4%) were considered nonneoplastic. Because incorrect classification of a tumor could lead to inappropriate therapeutic intervention and prognostication, the results of our study clearly illustrate the importance of revision and further diagnosis of "unclassified STSs" in dogs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".