MétaCan
Menu
Back to cohort
Record W4214668991 · doi:10.1002/vrc2.326

Primary mediastinal spindle cell sarcoma in a dog

2022· article· en· W4214668991 on OpenAlexaboutno aff
Daniel C. Lomas, Katrina Garrett, David Taylor, M. Havlicek, Paul Jenkins

Bibliographic record

VenueVeterinary Record Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHistopathologyMediastinumSarcomaSpindle cell sarcomaThoracotomyThorax (insect anatomy)Soft tissue sarcomaPleural effusionRespiratory distressSoft tissuePathologyThymomaRadiologyRhabdomyosarcomaSurgeryAnatomy

Abstract

fetched live from OpenAlex

Abstract Soft tissue sarcomas are frequently reported tumours in dogs and are most often located in the cutaneous and subcutaneous tissues. Primary thoracic soft tissue sarcomas are infrequently reported in veterinary literature. This case report describes the diagnosis and treatment of a primary mediastinal spindle cell sarcoma of a 7‐year‐old, male, neutered Labrador retriever. Staging was performed before surgery. Computed tomography revealed a large intrathoracic mass in the left ventral hemithorax. A left‐sided thoracotomy was performed and a large pedunculated mass originating from the caudal mediastinum was excised. Histopathology revealed a high‐grade, undifferentiated spindle cell sarcoma. Immunohistochemistry staining confirmed the mesenchymal origin of the mass. Adjuvant chemotherapy was administered. Thoracic radiology was performed 38 days after surgery and revealed pleural effusion and mass effect in the caudal thorax. The patient was euthanased 66 days after surgery due to acute respiratory distress.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
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 head0.056
GPT teacher head0.343
Teacher spread0.287 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueVeterinary Record Case ReportsSame topicVeterinary Oncology ResearchFrench-language works237,207