Epidemiology of haemangiosarcoma diagnosed in dogs under primary veterinary care in the United Kingdom: Frequency and risk factors
Bibliographic record
Abstract
Abstract Background This study aimed to estimate the frequency and risk factors for haemangiosarcoma diagnosis in dogs. Methods Cases diagnosed in UK primary care practices in 2019 were identified from VetCompass records, and potential risk factors were manually extracted. Two nested case-control studies were conducted: one including all diagnosed haemangiosarcoma with or without histopathological confirmation (clinical diagnosis), and another limited to histopathologically confirmed haemangiosarcoma. Logistic regression modelling was used to identify risk factors among dogs aged five years or older. Results Of 2,250,741 dogs of all ages, 801 were diagnosed with haemangiosarcoma in 2019, giving an annual incidence risk of 0.036% (95% confidence interval [CI] 0.033–0.038%). Of the 1,066,381 dogs aged at least five years, there were 790 incident cases (0.074%, 95% CI 0.069–0.079%). After excluding dogs that belonged to a small practice group, risk factor analysis compared 788 clinically diagnosed cases with 1,064,187 controls. Ten breeds had increased odds of clinical haemangiosarcoma diagnosis compared to crossbreed dogs. Breeds with the highest odds were Dogue de Bordeaux (OR 9.56, 95% CI 4.17–21.91), Flat Coated Retriever (OR 8.33, 95% CI 4.03–17.20) and German Shepherd Dog (OR 6.35, 95% CI 4.86–8.29). In the histopathological subset, the Flat Coated Retriever showed highest odds (OR 17.5, 95% CI 7.88–38.67). Compared to dogs aged 7 < 9 years old, older age was a significant risk factor in both clinical (11 < 13 years, OR 2.83, 95% CI 2.31–3.48) and histopathological diagnoses (9 < 11 years, OR 1.90, 95% CI 1.40–2.58). In the broader clinically diagnosed analysis, increased odds were also associated with neutered males (OR 1.49, 95% CI 1.17–1.90) and clinics in the least deprived areas (OR 1.66, 95% CI 1.18–2.34). Dogs attending clinics in mixed urban-rural areas had decreased odds of clinical haemangiosarcoma diagnosis compared to those in urban areas (OR 0.71, 95% CI 0.57–0.89). Conclusions Several strong breed associations for diagnosis of haemangiosarcoma were identified which can support earlier clinical suspicion and targeted diagnostics, especially where early signs are non-specific. Breed-related associations with haemangiosarcoma diagnosis may suggest a genetic predisposition which may be targeted through changes in breeding practices and promotion of outcrossing in high-risk breeds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".