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Record W978629428

CANINE ELBOW DYSPLASIA IN DIFFERENT BREEDS

2008· article· en· W978629428 on OpenAlexaboutno aff
T. Narojek, K. Fiszdon, Ewa Hanysz

Bibliographic record

VenueBulletin of the Veterinary Institute in Pulawy · 2008
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsElbowDysplasiaMedicineVeterinary medicineIncidence (geometry)OsteochondrosisOsteochondritisSurgeryPathology
DOInot available

Abstract

fetched live from OpenAlex

The analysis of elbow radiographs of 21 272 dogs, examined during 1988-2005, enabled us to estimate the frequency of canine elbow dysplasia in 90 breeds of dogs. Relation between breeds and incidence of elbow dysplasia was established statistically. The percentage of affected specimens was surprisingly low at 0.7% (150 dogs), and distinctly lower than the percentages reported by several authors in Germany, the USA, and other countries. The highest number of dogs affected was found in 47 (1.7% of total number studied) German Shepherds, in 25 Rottweilers (2%), and in 17 Dachshunds (0.5%). Breeds most often affected were of FCI group II, namely English Mastiffs (9%), Dogue de Bordeaux (8.5%), Neapolitan Mastiffs (8.3%), and Newfoundlands (4.6%). Elbow dysplasia was also found with significant frequency in breeds of the FCI group VIII – Labrador Retrievers (4.3%) and Golden Retrievers (4.4%). Fragmented coronoid process (FCP) was most frequently diagnosed (58.0% out of 150 affected specimens). Ununited anconeal process (UAP), was diagnosed in 32.0%. FCP and UAP together were present in 6.7% of the dysplastic specimens. No cases of osteochondritis dessecans and joint incongruity were found. UAP cases were most common in German Shepherds, while FCP occurred most frequently in Dogues de Bordeaux and Rottweilers.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.288
Teacher spread0.215 · 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.

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

Citations15
Published2008
Admission routes1
Has abstractyes

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