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Record W4406406044 · doi:10.3389/fvets.2024.1507861

High-field MRI findings in epileptic dogs with a normal inter-ictal neurological examination

2025· article· en· W4406406044 on OpenAlexaboutno aff
Stephanie Phillipps, Rita Gonçalves

Bibliographic record

VenueFrontiers in Veterinary Science · 2025
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
FundersUniversity of Liverpool
KeywordsIctalNeurological examinationMedicineEpilepsyNeurosciencePsychologyAnatomy

Abstract

fetched live from OpenAlex

Introduction Epilepsy is one of the most common chronic neurological conditions affecting dogs. Previous research exploring the likelihood of a structural cause of epilepsy specifically in dogs with a normal inter-ictal examination is limited to a small population of dogs using low-field MRI. The aims of this study were to establish high-field (1.0T and 1.5T) MRI findings in dogs presenting with epileptic seizures and a normal inter-ictal examination. Methods Medical records were retrospectively searched for dogs presenting with at least two epileptic seizure events more than 24 h apart. To be included in the study, patients had to have a normal neurological examination, high-field MRI of the brain and have had metabolic and toxic causes excluded. Results Four hundred and twelve dogs were eligible for inclusion. Crossbreeds were most commonly affected (n = 63, 15.3%) followed by Border collies (n = 39, 9.5%) and Labrador retrievers (n = 26, 6.3%). Seventy-six dogs (18.5%) had abnormalities detected on MRI, 60 (78.9%) of which were considered to be incidental. Overall, 16 dogs (3.9%) had a structural cause of their epileptic seizures including neoplasia (n = 13, 81.3%), anomalous (n = 2, 12.5%) and meningoencephalitis of unknown origin (MUO) (n = 1, 6.3%). When split into age group at first epileptic seizure structural lesions were documented in 0/66 dogs aged <1 year, 4/256 (1.6%) dogs aged ≥1 year ≤6 years (three neoplastic and one anomalous), 3/51 (5.9%) aged >6 years ≤8 years (two neoplastic and one MUO), and 9/39 (23.1%) dogs aged >8 years (eight neoplastic, one anomalous). Multivariate analysis identified two risk factors for structural disease: increasing age at first epileptic seizure (p < 0.001, OR = 4.390, CI 2.338–8.072) and a history of status epilepticus (p = 0.049, OR = 4.389, CI 1.010–19.078). Discussion Structural lesions are an uncommon cause of epilepsy at any age in dogs with a normal inter-ictal examination.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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