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Record W273178577

Estudo retrospectivo de 180 cães com displasia coxofemoral atendidos no Hospital Veterinário da Unesp Botucatu

2013· article· pt· W273178577 on OpenAlexaff
Bruno Watanabe Minto, Beatriz P. Monteiro, Cláudia Valéria Seullner Brandão, Carlos Roberto Padovani, Maria Jaqueline Mamprim, Fabrícia Geovânia Fernandes Filgueira

Bibliographic record

VenueVeterinária e Zootecnia · 2013
Typearticle
Languagept
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineHip dysplasiaAsymptomaticBreedRadiographyBody weightOsteoarthritisPediatricsSurgeryInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

Hip dysplasia (HD) is one of the most important canine orthopedic disease because of its high occurrence and severe consequences in the quality of life of many dogs. The aim of this study was to evaluate epidemiologically and statistically 180 dysplastic dogs attended at the Veterinary Teaching Hospital, Sao Paulo State University, in a five year period. It was correlated the severity of clinical signs and the HD from radiographs of 120 animals. Sex, body weight and age of animal at time of diagnosis were not a risk factor for severity of HD or clinical signs. More than 50% of the Rottweillers, German Shepherds, Labradors Retrievers and Pit Bulls presented with severe HD. Mean body weight of dogs with severe HD was 34.31kg.  From 180 evaluated dogs, 22.22% had osteoarthritis at time of diagnosis. Around 45% of pure breed dogs presented with severe HD versus 27.27% of mix breed dog. A total of 42.11% of clinically asymptomatic dogs had severe radiographic signs of HD. There was no significant correlation between the severity of clinical signs and radiographic lesions

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), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.017

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 head0.025
GPT teacher head0.282
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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