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Record W4408920016 · doi:10.46419/vs.56.6.4

Radiological Assessment of DISH in the Lumbar and Lumbosacral Vertebrae of Dogs

2025· article· en· W4408920016 on OpenAlexaboutno aff
Dženita Hadžijunuzović, Nejra Hadžimusić

Bibliographic record

VenueVeterinarska stanica · 2025
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsLumbosacral jointRadiological weaponLumbar vertebraeMedicineLumbarAnatomySurgery

Abstract

fetched live from OpenAlex

Diffuse idiopathic skeletal hyperostosis (DISH) is a non-inflammatory disorder characterised by extensive bony proliferation along the axial skeleton. This study evaluates the radiological features of DISH in the lumbar and lumbosacral vertebrae of dogs. Radiographic records from the University of Sarajevo, Faculty of Veterinary Medicine were analysed for dogs older than one year over a 12-month period. Thirteen cases of DISH were identified, predominantly in large breed dogs, with no cases observed in small breeds. Mixed-breed dogs, Labrador Retrievers, and Boxers were the most frequently affected breeds, and incidence was highest in dogs aged 7–10 years. The hallmark radiographic findings included flowing calcifications along the ventrolateral aspects of at least four contiguous vertebral bodies, with preservation of disc height. The most pronounced changes were observed between the L3 and L4 vertebrae. These findings highlight the importance of recognising DISH as a distinct entity in veterinary practice to avoid misclassification as severe spondylosis.

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 score0.003
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.375
Teacher spread0.330 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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