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Record W1827079147

Clinical and magnetic resonance imaging (MRI) findings in 26 dogs with canine osseous-associated cervical spondylomyelopathy.

2014· article· en· W1827079147 on OpenAlexaff
Vishal D. Murthy, Luís Gaitero, Gabrielle Monteith

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMagnetic resonance imagingMedicineRadiologySpinal cord compressionSpinal cordMultiple sclerosisPathology
DOInot available

Abstract

fetched live from OpenAlex

The potential link between degenerative changes seen on magnetic resonance imaging (MRI) in osseous-associated cervical spondylomyelopathy (OA-CSM) and clinical signs has not been explored. Our goal was to retrospectively evaluate MRI findings, while investigating potential correlations between these changes, signalment, and clinical signs. Twenty-six dogs diagnosed with OA-CSM were included in the study. Clinical signs were converted into a Modified Frankel Score (MFS) and MRI findings were assessed and graded. Giant breeds had multiple compressed sites and presented at a younger age than large breeds, suggesting a different underlying pathophysiology. Spinal cord compression, most commonly bilateral, was present in 36.8% of intervertebral spaces. Synovial fluid loss and articular process sclerosis were the most common degenerative changes. Most dogs showed identical MFS scores, and no significant correlations were found between MFS and MRI changes. More detailed functional scales should be used to investigate this in the future.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.028
GPT teacher head0.269
Teacher spread0.242 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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