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Record W2997242319 · doi:10.3389/fvets.2019.00424

Goniometric Assessment in French Bulldogs

2019· article· en· W2997242319 on OpenAlexaboutno aff
Maíra Rezende Formenton, Lidiane Gonçalves de Lima, Flávia Gardilin Vassalo, Jean Guilherme Fernandes Joaquim, Laryssa Petrocini Rosseto, Denise Tabacchi Fantoni

Bibliographic record

VenueFrontiers in Veterinary Science · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsGoniometerTarsus (eyelids)MedicineRange of motionInclination angleRadiographyOrthodonticsTarsal JointElbowHip flexionAnatomyPhysical therapySurgeryMathematics

Abstract

fetched live from OpenAlex

Goniometry is a low-cost, user-friendly and widely available technique used by different veterinary medicine professionals to estimate joint range of motion (ROM). Studies providing breed-specific reference ranges for goniometric measurements are scarce and there is a lack of information regarding joint angles in French Bulldogs. This prospective study set out to determine normal ROM for the carpus, elbow, shoulder, tarsus, stifle and hip joints in healthy, adult French Bulldogs using goniometry. We hypothesized ROM would be similar in this and other dog breeds. Twenty dogs met the inclusion criteria. Sample size was calculated using power analysis based on previous studies. Goniometric measurements were made by a single examiner. Limbs were measured in random order and three measurements made per joint. Dogs were not sedated. Joint angles measured in French Bulldogs in this study were similar to those reported in Labrador Retrievers (shoulder, carpal, and tarsal flexion), Rottweilers (shoulder, carpus, and hip flexion), and Dachshunds (hip, stifle, and tarsal flexion). Similar flexion angles and ROM were detected in right and left limb joints. Findings of this study suggest similar ROM in French Bulldogs and other dog breeds. Lack of radiographic assessment and the fact that goniometric measurements were made by a single examiner were the major limitations of this study.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.333
Teacher spread0.296 · 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.

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

Citations19
Published2019
Admission routes1
Has abstractyes

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