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Record W2981166014 · doi:10.3389/fvets.2019.00378

Prevalence of Canine Hip Dysplasia in Switzerland Between 1995 and 2016—A Retrospective Study in 5 Common Large Breeds

2019· article· en· W2981166014 on OpenAlexaboutno aff
Stefanie Ohlerth, Britta Geiser, Mark Flückiger, U Geissbühler

Bibliographic record

VenueFrontiers in Veterinary Science · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsBreedLabrador RetrieverHip dysplasiaMedicineVeterinary medicineGerman Shepherd DogPrevalencePopulationEpidemiologyAnimal scienceBiologyInternal medicineSurgeryEnvironmental healthRadiography

Abstract

fetched live from OpenAlex

Canine hip dysplasia (CHD) is a genetic disease, modulated by epigenetic and environmental factors. To decrease the prevalence of CHD, the hip joints of many pure breed dogs are radiographed to identify dysplastic dogs not qualified for breeding. It has been shown that both, prevalence and severity of CHD may be reduced on the basis of phenotypic i.e., radiographic selection of breeding animals. The method has been used in many countries for over 50 years. In the present study, severity and prevalence of CHD in five common large breeds in Switzerland were evaluated since 1995. Both, prevalence and severity of CHD dropped in each breed between the periods 1995-1999 and 2010-2016. The prevalence decreased in Golden Retrievers from 25 to 9% and in Labrador Retrievers from 16 to 3%, respectively. In the Flat-Coated Retriever, prevalence in general was low, decreasing from 6 to 3%. In the Bernese mountain dog and the German shepherd dog, a decrease from 21 to 12% and from 46 to 18%, respectively, was observed. However, the rather low overall rate of radiographed dogs (Retrievers: 11 to 18%, Bernese Mountain dogs: 23% and German Shepherd dogs: 31%) does not allow to draw reliable conclusions regarding the true prevalence of CHD for the entire population of these breeds in Switzerland.

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)0.002
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.027
GPT teacher head0.305
Teacher spread0.278 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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