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Record W4391442580 · doi:10.1002/vrc2.814

Diagnosis of lymphoma based on magnetic resonance imaging, analysis of cerebrospinal fluid, intraocular fluid and fine‐needle aspirate of a uveal mass lesion in a dog

2024· article· en· W4391442580 on OpenAlexaboutno aff
Koen M. Santifort, Marta Płonek, I. M. G. Kraijer‐Huver, Laurent Garosi, Tatiana R. Rothacker, Paul J. J. Mandigers

Bibliographic record

VenueVeterinary Record Case Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCerebrospinal fluidMagnetic resonance imagingPathologyLesionLymphomaMass/lesionRadiology

Abstract

fetched live from OpenAlex

Abstract A 10.5‐year‐old, male, entire labrador retriever dog was presented for chronic progressive ataxia and recent onset of a left‐sided head tilt and salivation. Neurological examination findings were deemed consistent with a multifocal brain and spinal cord neuroanatomic localisation. Ophthalmological examination of the right eye revealed pupil shape abnormality (dyscoria), a pale uveal mass lesion and white cellular sediment in the anterior chamber. Magnetic resonance imaging of the brain, eyes, thoracolumbar and lumbosacral spinal cord and surrounding structures showed multiple lesions affecting the central nervous system (brain), peripheral nervous system (left trigeminal nerve) and right eye. Cerebrospinal fluid, right eye anterior chamber fluid and fine‐needle aspirate biopsy of the uveal mass lesion yielded a cytological diagnosis of lymphoma (suspected large granular lymphocytic). The dog was discharged with palliative treatment and euthanased at home 4 days later. This case report documents the cytological diagnosis of lymphoma in the cerebrospinal fluid, intraocular fluid and fine‐needle aspirate of a uveal mass in a dog.

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)0.001
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 score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.334
Teacher spread0.300 · 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
Published2024
Admission routes1
Has abstractyes

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