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Record W1977545313 · doi:10.1111/avj.12211

Late‐onset cerebellar abiotrophy in a Labrador Retriever

2014· article· en· W1977545313 on OpenAlexaboutno aff
A Bertalan, Glass En, Marc Kent, Alexander de Lahunta, Charles W. Bradley

Bibliographic record

VenueAustralian Veterinary Journal · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsCerebellumBrainstemMedicineAtaxiaNeurological examinationCerebellar ataxiaLabrador RetrieverMagnetic resonance imagingPathologyAnatomyAbnormalityNystagmusPhysical examinationCerebellar hemisphereRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

CASE REPORT: A 5-year-old female spayed Labrador Retriever was examined for a hindlimb gait abnormality. Initial neurological examination was consistent with vestibular dysfunction. Over the course of 1 year, signs progressed to reflect cerebellar ataxia, vertical nystagmus and delayed postural reactions in all limbs. At the initial examination, subjective evaluation of magnetic resonance imaging scan of the brain was considered normal. Repeat imaging at 1 year after initial examination revealed a reduction in the size of the cerebellum. Retrospectively, the size of the cerebellum on the initial MRI was small when assessed using an objective measurement algorithm. Postmortem histopathological evaluation of the brain showed diffuse degeneration of Purkinje cell neurones with secondary granule cell loss in the cerebellum, in addition to pigment inclusions in brainstem neurones. CONCLUSION: The clinical history and clinicopathological data are consistent with late-onset cerebellar abiotrophy, which has not previously been described in this breed.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.292
Teacher spread0.240 · 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 designCase report
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

Citations12
Published2014
Admission routes1
Has abstractyes

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