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Record W3034600244 · doi:10.5455/javar.2020.g413

Measurement of the tibial plateau angle of normal small-breed dogs and the application of the tibial plateau angle in cranial cruciate ligament rupture

2020· article· en· W3034600244 on OpenAlexaboutno aff
Beom Seo, In Seong Jeong, Zhenglin Piao, Minju Kim, Sehoon Kim, Md. Mahbubur Rahman, Nam Soo Kim

Bibliographic record

VenueJournal of Advanced Veterinary and Animal Research · 2020
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
FundersNational Research Foundation of KoreaChonbuk National UniversityNational Research Foundation
KeywordsBreedCruciate ligamentNeuteringStifle jointMedicineLabrador RetrieverBody weightVeterinary medicineCATSAnterior cruciate ligamentInternal medicineAnatomyAnimal scienceSurgeryBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: In Korea, small dogs are more common than large breeds. This study was performed to measure the influence of body weight, sex, breed, age, and cranial cruciate ligament rupture (RCCL) on the tibial plateau angle (TPA) in small-breed dogs. MATERIALS AND METHODS: A total of 274 dogs (221 normal dogs and 53 RCCL dogs) were selected for this study based on medical records. The TPA was measured from stifle joint radiographs. The dogs were divided according to body weight, sex (male and female; normal and neutered), age, breed, and RCCL, and the TPAs of the dogs were compared. RESULTS: < 0.001) than that of normal dogs (20.21° ± 0.32°), indicating that an increased TPA is associated with a higher risk for RCCL. Similar results were also observed among dogs with similar body weights, breeds, and ages for male and female RCCL dogs. CONCLUSION: This study suggested that the sex and neutering status of dogs could affect the TPA. This study also confirmed the use of TPA in the veterinary clinic as a possible indicator of RCCL, as the TPA is higher in RCCL dogs than in normal dogs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.120
GPT teacher head0.357
Teacher spread0.237 · 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 designBench or experimental
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

Citations13
Published2020
Admission routes1
Has abstractyes

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