Retrospective study of canine gastrointestinal tumors in Tokyo, Japan, 2012–2024
Bibliographic record
Abstract
Gastrointestinal (GI) tumors are common neoplastic diseases in dogs. However, epidemiological data on canine GI tumors in Japan are limited. The present study aimed to investigate the prevalence of GI tumors in Japan and assess the association of canine breed, age, sex, and anatomical location with the development of common tumor types. A total of 1,310 canine GI tumors that were histopathologically examined between 2012 and 2024 were retrospectively analyzed. The statistical methods included a contingency table analysis, multivariable logistic regression analyses, and Mann-Whitney U tests. The most frequent GI tumor was lymphoma (58.9%), followed by adenocarcinoma (16.2%) and adenoma (15.0%). Statistical examination revealed that Shiba dogs were predisposed to T-cell lymphoma, Miniature Dachshunds to colorectal B-cell lymphoma and colorectal adenoma, Jack Russell Terriers to adenoma, acinar adenocarcinoma, papillary adenocarcinoma and tubulopapillary carcinoma, French Bulldogs to gastric signet-ring cell carcinoma and plasmacytoma, and Shih Tzus to tubulopapillary adenocarcinoma. These breed predispositions to specific tumors may be unique to the Japanese canine population. To the best of our knowledge, this is the first large-scale epidemiological investigation of canine GI tumors in Japan. The epidemiological information from the present study will serve as a useful reference for clinical veterinarians to establish the differential diagnoses of canine GI tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".