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Record W2138427067

Incidence of canine hip dysplasia: a survey of 272 cases.

2010· article· en· W2138427067 on OpenAlexaboutno aff
Michael S. Simon, R. Ganesh, S. Ayyappan, G. D. Rao, Rajiv Kumar, Mallaiyan Manonmani, Bhajan Chandra Das

Bibliographic record

VenueVeterinary World · 2010
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHip dysplasiaIncidence (geometry)Labrador RetrieverBreedDysplasiaEpidemiologySurgeryRadiographyInternal medicineAnimal scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

A total of 272 cases of hip dysplasia were reviewed. A review of clinical cases presented with the clinical signs of hip dysplasia were referred to Radiology Unit of Madras Veterinary College, from May 2007-April 2009 was taken for this study.The incidence was highest in young animals of age group over three months to one year (52.94 percent). The breed-wise incidence was more common in Labrador Retriever (36.76 percent). Male dogs were found to be more affected (59.55 percent) than female dogs. Bilateral hip dysplasia was found to be more (88.60 percent) than unilateral. Among the unilateral hip dysplasia, left side was found to be more (54.83 percent) than right.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.097
GPT teacher head0.345
Teacher spread0.249 · 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 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

Citations5
Published2010
Admission routes1
Has abstractyes

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