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Record W3193519457 · doi:10.3389/fvets.2021.715908

Case Report: Metastatic Parosteal Osteosarcoma in a Dog

2021· article· en· W3193519457 on OpenAlexaboutno aff
Sarah K. Samuels, Matthew R. Cook, Eric Green, Ryan Jennings, Roy R. Pool, Vincent A. Wavreille, William C. Kisseberth, Laura E. Selmic

Bibliographic record

VenueFrontiers in Veterinary Science · 2021
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
FundersOhio State University
KeywordsMedicineHumerusNodule (geology)Thorax (insect anatomy)ElbowRadiologyLesionMetastasisRadiographyScapulaAnatomyPathologyCancer

Abstract

fetched live from OpenAlex

This case report describes a rare form of malignant bone tumor in an 8-year-old Labrador retriever. This dog initially presented for evaluation of a right distal humeral mass. Radiographs of the right elbow and thorax were performed, revealing a smooth mineralized mass adjacent to the lateral aspect of the distal humerus and a 5mm pulmonary nodule. Computed tomography (CT) of the humerus and thorax showed a smooth mineralized lesion adjacent to the lateral humeral epicondyle, and a right cranial lung lobe nodule with a thin mineral rim. Surgical biopsies of both lesions were diagnostic for parosteal osteosarcoma (POSA). The dog was then treated with stereotactic body radiation therapy (SBRT) which controlled the dog's discomfort for 14 months until he became progressively painful and subsequently had his right forelimb amputated. This case report is the first to document the CT imaging characteristics of a metastatic appendicular POSA in a dog and the first dog described with POSA treated with SBRT. The dog lived for 623 days after histopathologic diagnosis and 849 days after initial presentation with pulmonary metastatic disease.

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)0.002
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 score0.005
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.069
GPT teacher head0.385
Teacher spread0.316 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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