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Record W2986280800 · doi:10.1186/s12917-019-2154-8

Composite lymphoma of concurrent T zone lymphoma and large cell B cell lymphoma in a dog

2019· article· en· W2986280800 on OpenAlexafffund
Arata Matsuyama, Dorothee Bienzle, Danielle Richardson, Nariman Deravi, Mei-Hua Hwang, Nikos Darzentas, Stefan M. Keller

Bibliographic record

VenueBMC Veterinary Research · 2019
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Guelph
FundersMinisterstvo Zdravotnictví Ceské RepublikyUniversity of Guelph
KeywordsLymphomaImmunophenotypingPathologyBCL10Canine LymphomaHistopathologyBiologyB-cell lymphomaB cellFlow cytometryImmunologyMedicineAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Evolution of indolent to aggressive lymphoma has been described in dogs but is difficult to distinguish from the de novo development of a second, clonally distinct lymphoma. Differentiation of these scenarios can be aided by next generation sequencing (NGS)-based assessment of clonality of lymphocyte antigen receptor genes. CASE PRESENTATION: An 8-year-old male intact Mastiff presented with generalized lymphadenomegaly was diagnosed with nodal T zone lymphoma (TZL) based on cytology, histopathology, immunohistochemistry and flow cytometry. Thirteen months later, the dog re-presented with progressive lymphadenomegaly, and based on cytology and flow cytometry, a large B cell lymphoma (LBCL) was diagnosed. Sequencing-based clonality testing confirmed the de novo development of a LBCL and the persistence of a TZL. CONCLUSIONS: The occurrence of two distinct lymphoid neoplasms should be considered if patient features and tumor cytomorphology or immunophenotype differ among sequential samples. Sequencing-based clonality testing may provide conclusive evidence of two concurrent and distinct clonal lymphocyte populations, termed most appropriately "composite lymphoma".

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)0.003
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.090
GPT teacher head0.400
Teacher spread0.311 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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