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Record W2046681069 · doi:10.1136/inpract.29.2.66

Hip dysplasia in dogs: treatment options and decision making

2007· article· en· W2046681069 on OpenAlexaboutno aff
Sandra Corr

Bibliographic record

VenueIn Practice · 2007
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsHip dysplasiaMedicineDysplasiaOsteoarthritisDiseaseSurgeryRadiographyInternal medicinePathology

Abstract

fetched live from OpenAlex

HIP dysplasia remains a common orthopaedic disease of dogs despite many years of selective breeding based on early detection of affected animals through the British Veterinary Association/Kennel Club (BVA/KC) Hip Dysplasia Scheme. While selective breeding can alter an animal's genes, factors such as diet, bodyweight and exercise have a major influence on the phenotypic expression of an individual's genotype. For example, labrador retrievers fed 25 per cent less than littermates fed ad libitum have been found to have a lower frequency and severity of hip dysplasia and subsequent osteoarthritis. Although controversial, it has been suggested that as few as 24 per cent of young dogs with severe radiographic signs of hip dysplasia will actually develop clinically significant hip disease if managed appropriately. This can make it difficult to determine whether an individual dog should be managed conservatively or surgically and, if the latter, when the most appropriate time is to perform that surgery. This article reviews the current literature on the treatment of dogs with hip dysplasia and discusses the indications for surgical management of the condition.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.074
GPT teacher head0.412
Teacher spread0.338 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2007
Admission routes1
Has abstractyes

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