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Record W4392914263 · doi:10.1590/1678-5150-pvb-7318

Histopathological characterization and analysis of cell proliferation in 162 cases of canine subcutaneous mast cell tumors in Brazil

2024· article· en· W4392914263 on OpenAlexaboutno aff
Paulo César Jark, F. S. Barros, T. L. APEL, Eduardo G. Paula, Pedro Luiz Porfírio Xavier, Taismara Kustro Garnica, A Ferrero, Heidge Fukumasu, Felipe Augusto Ruiz Sueiro

Bibliographic record

VenuePesquisa Veterinária Brasileira · 2024
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsLabrador RetrieverSubcutaneous tissueMast cellMedicinePathologyBreedMitotic indexSubcutaneous injectionIncidence (geometry)BiologyInternal medicineMitosisImmunology

Abstract

fetched live from OpenAlex

ABSTRACT: There are limited publications about canine subcutaneous mast cell tumors (MCT). International studies have shown that subcutaneous MCT has longer survival times than cutaneous MCT, with lower recurrence and metastasis rates. In addition, subcutaneous MCT has a specific histopathological classification (circumscribed, combined, or infiltrative pattern). Our study evaluated 162 cases of subcutaneous MCT diagnosed from 2014 to 2017 in Brazil. The mean age of the animals was 8.6 years, with a predominance of females and higher incidence in dogs with mixed breed (n=40), followed by Boxer (n=20), Labrador Retriever (n=14), Golden Retriever (n=11) and Pug (n=10). Regarding histopathological characterization, the most common infiltrative pattern represented 54.3% of cases, followed by circumscribed (34.8%) and combined (11%) patterns. The mean mitotic index (MI) was 1.04, with 93.9% of cases presenting MI≤4 and 53.1% MI=0. The data found in this Brazilian study regarding subcutaneous MCT does not differ from those described in American studies, suggesting similar genetic and epidemiological factors. The evaluated proliferation indices suggest that subcutaneous MCT presents slow progression and should be evaluated as a distinct form of cutaneous 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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.330
Teacher spread0.295 · 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
Published2024
Admission routes1
Has abstractyes

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