Refining the “double two‐thirds” rule: Genotype‐based breed grouping and clinical presentation help predict the diagnosis of canine splenic mass lesions in 288 dogs
Bibliographic record
Abstract
Prediction of the likely histopathological diagnosis of canine splenic masses can guide appropriate decision-making. This study explores the predictive effect of breed and clinical presentation on the diagnosis of a canine splenic mass. Records from the Royal Veterinary College, United Kingdom (2007-2017) were reviewed. Dogs with a histopathologic or cytologic diagnosis from a splenic mass, or imaging findings consistent with disseminated metastatic disease, were included. Signalment, physical examination, haematology results, imaging findings and pathology reports were recorded. Breeds were grouped according to several permutations of their phenotype and then by clustering of breeds based on single nucleotide polymorphism analysis. Binary logistic regression was performed to identify predictors of malignancy and haemangiosarcoma. Two hundred and eighty-eight dogs were identified: 27% female and 63% male, 21% entire and 79% neutered; German Shepherd was the most common breed (11%). Median age was 10 years and median bodyweight 25 kg. Thirty-eight percent of dogs presented with haemoabdomen; a splenic mass was found incidentally in 28%. Sixty percent had a malignant tumour of which haemangiosarcoma comprised 66%. On multivariable analysis, genotype-based breed group (P = .004), haemoabdomen (P < .001) and neutrophil count (P = .025) predicted malignancy, and genotype-based breed group (P < .001) and haemoabdomen (P < .001) predicted haemangiosarcoma. Genotype-based breed group and occurrence of haemoabdomen may have predictive value to diagnose malignant splenic masses and more specifically haemangiosarcoma. The effect of genotype-based breed grouping was a superior predictor of the diagnosis of a canine splenic mass lesion compared with all phenotype-based groupings tested.
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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 | 0.006 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".