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Record W4322500457 · doi:10.3390/ani13050862

Neuropathology of Central and Peripheral Nervous System Lymphoma in Dogs and Cats: A Study of 92 Cases and Review of the Literature

2023· article· en· W4322500457 on OpenAlexaboutno aff
Niccolò Fonti, Francesca Parisi, Çağla Aytaş, S. Degl’Innocenti, Carlo Cantile

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
FundersUniversità di Pisa
KeywordsCATSForebrainLymphomaNeuropathologyPathologyPathologicalCentral nervous systemMeningesMedicineHistopathologyPrimary central nervous system lymphomaInternal medicineDisease

Abstract

fetched live from OpenAlex

The literature about nervous system lymphoma (NSL) in dogs and cats is fragmentary, based on a few case series and case reports with heterogeneous results. The aim of our study was to retrospectively analyze 45 cases of canine and 47 cases of feline NSL and compare our results with previously reported data, also providing an extensive literature review. Breed, age, gender, clinical signs, type, and neurolocalization were recorded for each case. The pathological patterns and phenotype were assessed by histopathology and immunohistochemistry. The occurrence of central and peripheral NSL was similar between the two species in both primary and secondary types. NSL occurred with a slightly higher prevalence in Labrador Retrievers, and spinal cord lymphoma (SCL) was associated with young age in cats. The most frequent locations were the forebrain in dogs and the thoracolumbar segment in cats. Primary central nervous system lymphoma (CNSL) in cats most frequently involved the forebrain meninges, particularly as a B-cell phenotype. Peripheral NSL mostly affected the sciatic nerve in dogs and had no preferred location in cats. Nine different pathological patterns were identified, with extradural as the most prevalent SCL pattern in both species. Finally, lymphomatosis cerebri was described for the first time 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.345
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2023
Admission routes1
Has abstractyes

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