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Record W4394533743 · doi:10.6084/m9.figshare.19996621

Nasal leiomyosarcoma in a Quarter Horse

2022· dataset· en· W4394533743 on OpenAlexaboutno aff
Millena de Oliveira Firmino, Rodrigo Cruz Alves, Yanca Góes dos Santos Soares, Rodolfo Monteiro Bastos, Daniel de Medeiros Assis, Deborah Castro, Glauco José Nogueira de Galiza, Antônio Flávio Medeiros Dantas

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsHorseQuarter (Canadian coin)BiologyGeographyArchaeologyPaleontology

Abstract

fetched live from OpenAlex

ABSTRACT: We described the clinical and anatomopathological findings observed in a case of nasal leiomyosarcoma in a five-year-old male Quarter Mile horse, whose main complaints were decreased sports performance and bilateral purulent nasal discharge. The nodule was observed in the nasal cavity, obstructing the left nostril and associated with purulent drainage. The nodule was of irregular shape and yellow color, measuring 19.4 cm × 6.9 cm × 4.3 cm in size, with coalescent multifocal areas that were brownish, friable, opaque, and fetid. When cut, the surface was compact, grayish-white, and smooth with yellow, friable, irregular multifocal areas, measuring 1-3.2 cm in diameter. Histopathological examination showed spindle-shaped neoplastic cells, which was negative on Masson’s trichromic stain. A diagnosis of leiomyosarcoma was established based on the morphotintorial aspects of neoplastic cells and confirmed through immunohistochemistry, with positive immunostaining for antibodies 1A4, HHF35, desmin, and S100. Leiomyosarcoma primarily affects the nasal cavity of horses and should be included in the differential diagnosis of diseases that affect the nasal cavity and cause nasal obstruction associated with dyspnea.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.9710.008

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 head0.086
GPT teacher head0.384
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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