MétaCan
Menu
Back to cohort

MAGNETIC RESONANCE IMAGING OF SPONGY DEGENERATION OF THE CENTRAL NERVOUS SYSTEM IN A LABRADOR RETRIEVER

2001· article· en· W2084666685 on OpenAlexaboutno aff
Christopher L. Mariani, Roger M. Clemmons, John P. Graham, Lynette Phillips, Cheryl L. Chrisman

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2001
Typearticle
Languageen
FieldNeuroscience
TopicNeurological diseases and metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsTetraparesisMedicineLabrador RetrieverAtaxiaMagnetic resonance imagingThalamusAnatomyCentral nervous systemPathologyCerebellumDegeneration (medical)Radiology

Abstract

fetched live from OpenAlex

A 7-month-old, neutered female Labrador Retriever was evaluated for tetraparesis and subtle cerebellar dysfunction. Clinical signs progressed over a period of 6 weeks to severe ataxia, hypermetria, intention tremors, and finally non-ambulatory tetraparesis. On magnetic resonance imaging of the brain there were large, bilaterally symmetrical, ovoid lesions in the region of the deep cerebellar nuclei that were hyperintense on T2-weighted and proton density images and hypointense on T1-weighted images. There were similar but smaller bilaterally symmetrical lesions present within the thalamus. Euthanasia was performed and lesions consistent with the previously described spongy degeneration of Labrador Retrievers were identified. This disease and its relation to similar human heritable leukodystrophies are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.404

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 head0.020
GPT teacher head0.238
Teacher spread0.219 · 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

Citations20
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueVeterinary Radiology & UltrasoundSame topicNeurological diseases and metabolismFrench-language works237,207