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Record W2322926465 · doi:10.12935/jvma1951.58.829

Fibrocartilaginous Embolism in a Dog

2005· article· en· W2322926465 on OpenAlexaboutno aff
Munekazu NAKAICHI, Yuji C. Sasaki, Keiko Hasegawa, Masahiro Morimoto, Toshiharu Hayashi, Kazuhito ITAMOTO, Satoshi UNE, Yasuho TAURA

Bibliographic record

VenueJournal of the Japan Veterinary Medical Association · 2005
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLesionTetraplegiaSpinal cordForelimbEmbolismNeurological examinationPathologicalMidbrainInfarctionHistopathological examinationRadiographyPathologyAnatomyRadiologySpinal cord injuryCentral nervous systemSurgeryMyocardial infarctionCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract. A five-year-old female Labrador Retriever was referred for the diagnosis of acute onset of tetraplegia with a three-week history. A neurological examination demonstrated lower motor neuron signs on the left forelimb and upper neuron signs on the both hindlimbs, suggesting a lesion at the spinal cord segments C6-T2. Radiographs of the cervical area did not show any pathological changes. MR images demonstrated a lesion at the C6, as a high intensity area on T2-weighted images, with minimal contrast-enhancement both on T1- and T2-weighted images. As a fibrocartilaginous embolism was the most likely diagnosis for the tetraplegia of the dog, a poor prognosis was given. Euthanasia and necropsy were performed and a definitive diagnosis of fibrocartilaginous embolism was made through a histopathological examination, since an ischemic infarction with a fibrocartilaginous tissue in the vessels around the lesion was observed.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.321
Teacher spread0.287 · 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

Citations3
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Japan Veterinary Medical AssociationSame topicVeterinary Orthopedics and NeurologyFrench-language works237,207