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Record W2055923481 · doi:10.2460/javma.2005.227.1109

Prevalence of cranial cruciate ligament rupture in a population of dogs with lameness previously attributed to hip dysplasia: 369 cases (1994–2003)

2005· article· en· W2055923481 on OpenAlexaboutno aff
Michelle Y. Powers, Steven A. Martinez, James D. Lincoln, Cara J. Temple, Arthur Arnaiz

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2005
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLamenessLabrador RetrieverCruciate ligamentGerman Shepherd DogHip dysplasiaStifle jointSurgeryRadiographyAnterior cruciate ligament

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the prevalence of cranial cruciate ligament rupture (CCLR) in dogs with lameness previously attributed to canine hip dysplasia (CHD). DESIGN: Retrospective study. ANIMALS: 369 client-owned dogs. PROCEDURES: Hospital medical records from 1994 to 2003 were reviewed for dogs in which the referring veterinarian had diagnosed hip dysplasia or hip pain. Dogs were designated as having hind limb lameness because of partial or complete CCLR or CHD. RESULTS: 8% of dogs were sexually intact females, 43% were spayed females, 14% were sexually intact males, and 35% were castrated males. Mean age was 3.8 years (range, 3 months to 15 years). The most common breeds were the Labrador Retriever (21%), German Shepherd Dog (13%), and Golden Retriever (11%). The prevalence of CCLR as the cause of hind limb lameness was 32% (95% confidence interval, 27.2% to 36.8%). The distribution of CCLR among hind limbs was left (29%), right (28%), and bilateral (43%). Of 119 dogs with CCLR, 94% had concurrent radiographic signs of CHD, 92% had stifle joint effusion, and 81% had a cranial drawer sign. CONCLUSIONS AND CLINICAL RELEVANCE: On the basis of the high prevalence of CCLR in dogs referred for lameness because of CHD, it is important to exclude other sources of stifle joint disease before making recommendations for treatment of CHD.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.320
Teacher spread0.289 · 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

Citations50
Published2005
Admission routes1
Has abstractyes

Explore more

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