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Record W1778454304 · doi:10.3415/vcot-14-03-0051

Kinematic gait analysis of the canine thoracic limb using a six degrees of freedom marker set

2014· article· en· W1778454304 on OpenAlexaboutno aff
Alexandre Caron, A. Caley, M. Farrell, N. Fitzpatrick

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2014
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKinematicsGaitGait analysisPhysical medicine and rehabilitationAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine if the use of a six degrees of freedom marker set would allow new kinematic data of the canine thoracic limbs to be calculated. To identify any significant differences in thoracic limb gait patterns in all planes of motion, between the normal canine population and patients with confirmed medial coronoid disease (MCD). METHOD: Two groups of dogs were selected representing the normal Labrador Retriever population (n = 13) and Labrador Retrievers with confirmed MCD (n = 13). Normal dogs had "normal" hip and elbow radiographic scores in line with the International Elbow Working Group and British Veterinary Association guidelines. Medial coronoid disease was confirmed using arthroscopy after kinematic analysis was performed with a six degrees of freedom marker set. RESULTS: The diseased elbow was nine degrees more extended between 43%-55% of the gait cycle and 16° more supinated prior, early during and after foot strike. The antebrachium was nine degrees more supinated during foot strike and three degrees more abducted during early stance. None of the other parameters were significantly different. CLINICAL SIGNIFICANCE: The use of a six degrees of freedom marker set made it possible for the elbow and antebrachium to be reliably tracked in more than one plane of motion. Significant differences were identified between the normal canine population and those affected by MCD. These data may help elucidate biomechanical factors contributing to aetiopathogenesis of MCD.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.154
GPT teacher head0.361
Teacher spread0.207 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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