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Record W4383673587 · doi:10.52973/rcfcv-e33244

Concordance between clinical presentation and histopathological staging canine mammary tumors

2023· article· en· W4383673587 on OpenAlexaboutno aff
Alicia Decuadro, A. Benech, Silvia Llambí, R. Gagliardi

Bibliographic record

VenueRevista Científica de la Facultad de Ciencias Veterinarias · 2023
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHistopathologyConcordanceStage (stratigraphy)Labrador RetrieverPathologyPapillomaInternal medicineBiology

Abstract

fetched live from OpenAlex

In this work it was examined the concordance between clinical staging and histopathological staging of mammary tumors in 32 female dogs. It was observed that the average age of presentation of the pathology was 9 years (ranged from 6 to 12 years). The most affected mammary glands were the caudal abdominal and the inguinal, 20 out of 32 female dogs had multiple tumors (62%), and 38% single tumors. Regarding the breeds, the most frequent ones were mixed breeds, Poodle, Cimarron (native breed of Uruguay) and Labrador Retriever. Of the 32 female dogs with breast tumors studied, 65% had histopathological diagnosis of malignant tumor, while 35% had benign tumors. Clinical staging data showed that 64% of the cases with benign tumors were in stage I (1 to 3 cm) and 36% were in stage II (3 to 5 cm). Among those diagnosed with malignant tumors, 10% were in stage V, 57% in stage III, 9% in stage II, and 24% in stage I. There were no animals in stage IV. The most frequently found malignant tumors were tubular carcinoma and complex carcinoma, followed by solid and tubulopapillary carcinomas. Within the benign tumors, complex adenoma was the most frequent, followed by benign mixed tumor and simple ductal papilloma. The concordance between clinical staging and histopathology was low, as we could observe both benign T2 (3 to 5 cm) and malignant T1 (1 to 3 cm) 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.096
GPT teacher head0.425
Teacher spread0.329 · 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.

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRevista Científica de la Facultad de Ciencias VeterinariasSame topicVeterinary Oncology ResearchFrench-language works237,207