Postmortem Evaluation of 435 Cases of Intracranial Neoplasia in Dogs and Relationship of Neoplasm with Breed, Age, and Body Weight
Bibliographic record
Abstract
BACKGROUND: Intracranial neoplasia of dogs is frequently encountered in veterinary medicine, but large-scale studies on prevalence are lacking. OBJECTIVES: To determine the prevalence of intracranial neoplasia in a large population of dogs examined postmortem and the relationship between breed, age, and weight with the presence of primary intracranial neoplasms. ANIMALS: All dogs that underwent postmortem examination from 1986 through 2010 (n = 9,574), including dogs with a histopathologic diagnosis of primary (n = 227) and secondary (n = 208) intracranial neoplasia. METHODS: Retrospective evaluation of medical records from 1986 through 2010. RESULTS: Overall prevalence of intracranial neoplasia in this study's population of dogs was 4.5%. A statistically significant higher prevalence of primary intracranial neoplasms was found in dogs with increasing age and body weights. Dogs ≥15 kg had an increased risk of meningioma (odds ratio 2.3) when compared to dogs <15 kg. The Boxer, Boston Terrier, Golden Retriever, French Bulldog, and Rat Terrier had a significantly increased risk of primary intracranial neoplasms while the Cocker Spaniel and Doberman Pinscher showed a significantly decreased risk of primary intracranial neoplasms. CONCLUSIONS AND CLINICAL IMPORTANCE: Intracranial neoplasia in dogs might be more common than previous estimates. The study suggests that primary intracranial neoplasia should be a strong differential in older and larger breed dogs presenting with signs of nontraumatic intracranial disease. Specific breeds have been identified with an increased risk, and others with a decreased risk of primary intracranial neoplasms. The results warrant future investigations into the role of age, size, genetics, and breed on the development of intracranial neoplasms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".