MétaCan
Menu
Back to cohort
Record W2205792154

Short communication: Canine hip dysplasia in Tibetan terriers

2015· article· en· W2205792154 on OpenAlexaboutno aff
H. Humphreys, Neil McEwan

Bibliographic record

VenueVeterinary Record · 2015
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsBreedHeritabilityHip dysplasiaDemographyBiologyAlleleGeneticsMedicineGeneSurgery
DOInot available

Abstract

fetched live from OpenAlex

CANINE hip dysplasia (CHD) arises from incorrect coxofemoral joint development in dogs. CHD has been described in many dog breeds, but is generally considered a problem associated with larger breeds, suggesting CHD may have a genetic basis. This has resulted in several investigations into breed-specific studies (Wood and others, 2000, Lewis and others 2010) which have supported the hypothesis that CHD arises due to a combination of polygenic interactions (Wilson and others 2011). These have resulted in a variety of conclusions: differences in heritability scores between breeds, for example, labrador retrievers (Wood and others 2002) and Gordon setters (Wood and others 2000); reports of differences between left and right hips (Tsai and others 2007) with others reporting symmetry of hip scores (Wilson and others 2011) and differences in maternal versus paternal effects (Wood and others 2000). These differences between breeds suggest that there may be minor differences in the major causal genes/alleles in different breeds, meaning that it is essential to study CHD in as wide a range of breeds in order that the condition may be better understood at the species level. As mentioned above, although CHD is often perceived to be a problem affecting larger dogs, it is not restricted to larger breeds and can be found in smaller breeds too. The current work examines the condition in an example of a smaller breed; Tibetan terriers.....

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
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 head0.169
GPT teacher head0.357
Teacher spread0.188 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueVeterinary RecordSame topicVeterinary Orthopedics and NeurologyFrench-language works237,207