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Record W2110544750 · doi:10.1093/jhered/esp088

A Candidate Gene Study of Canine Joint Diseases

2009· article· en· W2110544750 on OpenAlexaboutno aff
Dylan N. Clements, A. D. Short, A. Barnes, L. J. Kennedy, John Ferguson, Steven J. Butterworth, N. Fitzpatrick, M. J. Pead, David Bennett, John Innes, Stuart Carter, William Ollier

Bibliographic record

VenueJournal of Heredity · 2009
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research Council
KeywordsCandidate geneSingle-nucleotide polymorphismBiologyHaplotypeOsteoarthritisGenotypeBreedAlleleHip dysplasiaPopulationGeneticsGeneMedicinePathologyRadiographySurgery

Abstract

fetched live from OpenAlex

Canine osteoarthritis (OA) commonly occurs in association with articular diseases, such as hip dysplasia (HD), elbow dysplasia (ED), or cranial cruciate ligament rupture (CCLR). We hypothesized that a common genomic risk for the development of canine joint disease and canine OA would be identified by evaluating the allele frequencies of candidate gene single nucleotide polymorphisms (SNPs) in dogs with OA associated with different articular diseases when compared with a general population of breed-matched dogs. DNA was extracted from blood samples obtained from Labrador Retrievers and Golden Retrievers surgically treated for ED, HD, and CCLR and confirmed to have radiographic evidence of OA. One hundred and thirteen SNPs in 20 candidate genes were genotyped. No significant associations were identified for SNPs or haplotypes in the candidate genes for the diseases evaluated. The candidate gene approach for the study of genetic association is unlikely to be successful for complex canine diseases such as OA without prior trait mapping evaluation.

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 score0.596
Threshold uncertainty score0.364

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 head0.066
GPT teacher head0.332
Teacher spread0.266 · 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

Citations19
Published2009
Admission routes1
Has abstractyes

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