MétaCan
Menu
Back to cohort
Record W4416290781 · doi:10.1292/jvms.25-0398

Retrospective study of canine gastrointestinal tumors in Tokyo, Japan, 2012–2024

2025· article· en· W4416290781 on OpenAlexaboutno aff
Kento ISHIKAWA, James Chambers, Kazuhiro Kojima, Sayoko Hanamoto, Ko Nakashima, Kazuyuki Uchida

Bibliographic record

VenueJournal of Veterinary Medical Science · 2025
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyRetrospective cohort studyAdenocarcinomaLabrador RetrieverColorectal cancerCancerLymphoma

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.064
GPT teacher head0.428
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Medical ScienceSame topicVeterinary Oncology ResearchFrench-language works237,207