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Record W4382786739 · doi:10.14405/kjvr.20230010

Diagnostic imaging findings of subcutaneous mast cell tumor in a dog

2023· article· en· W4382786739 on OpenAlexaboutno aff
S. B. Kwon, Min Jang, Sang‐Kwon Lee, Kija Lee

Bibliographic record

VenueKorean Journal of Veterinary Research · 2023
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsSubcutaneous fatMagnetic resonance imagingMedicineSubcutaneous tissuePathologySubcutaneous injectionSubcutaneous adipose tissueLabrador RetrieverMast cellRadiologyAdipose tissueInternal medicine

Abstract

fetched live from OpenAlex

A 5-year-old Labrador Retriever presented with a subcutaneous mass on the right scapular region with localized calor. On magnetic resonance imaging (MRI), a heterogeneously hypointense mass was seen on T1-weighted images (WI) and hyperintense on T2WI and fat-suppressed T2WI with marginal contrast enhancement on postcontrast T1WI and invasion into the surrounding tissue. Histopathological examination of the mass confirmed subcutaneous mast cell tumor (MCT) infiltrating to the adjacent subcutaneous fat. This is the first report to describe the MRI findings of subcutaneous MCT in veterinary medicine. This study suggests that MRI may help evaluate the extent of invasion of subcutaneous MCT.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.112
GPT teacher head0.432
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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
Published2023
Admission routes1
Has abstractyes

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