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Record W3004202282 · doi:10.3389/fvets.2020.00021

Magnetic Resonance Imaging Findings in 13 Neurologic Pot-Bellied Pigs

2020· article· en· W3004202282 on OpenAlexaff
Aude Castel, Vincent Doré, Mariana Vigeral, Silke Hecht

Bibliographic record

VenueFrontiers in Veterinary Science · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Dysraphism and Malformations
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineSyringomyeliaMagnetic resonance imagingRadiologyCoronal planeCauda equina syndromeSpinal cordPathology

Abstract

fetched live from OpenAlex

This study reports the magnetic resonance imaging (MRI) findings in thirteen pot-bellied pigs presented to our institution with neurological deficits. Nine pigs had abnormal MRI findings (7 with spinal cord localization and 2 with brain localization), with three of them having histopathological confirmation of the diagnoses. MRI diagnoses included a myopathy suspected to be secondary to Erysipelothrix rhusiopathiae, a round cell neoplasia involving the vertebral canal, myelomalacia, a cervical cyst like like extradural lesion, pelvic fracture with secondary cauda equina involvement, two cases of fibrocartilaginous embolism or acute non-compressive nucleus pulposus extrusion, multifocal brain infarcts, and a cystic fourth ventricle dilation resulting in obstructive hydrocephalus and syringomyelia. Four pigs had normal MRI studies, with one of them ultimately diagnosed with idiopathic vestibular disease. This retrospective study illustrates the wide variety of diagnoses achieved with MRI of the head or vertebral column in pigs, several of them having never been described in this species. Some of the conditions identified had a good outcome. This justifies using MRI as an ante-mortem diagnostic tool as it can provide relevant information about the prognosis which can significantly influence treatment recommendations. Our findings suggest that MRIshould be considered as a valuable imaging modality, when feasible, in pigs with neurological deficits.

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 score0.079
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.021
GPT teacher head0.256
Teacher spread0.235 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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