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Record W273832776

Evaluation of a novel accelerometer for kinetic gait analysis in dogs.

2014· article· en· W273832776 on OpenAlexaff
Kyle Clark, Charles Caraguel, Lorne Leahey, Romain Béraud

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsAccelerometerGaitConcordanceGait analysisLimits of agreementPhysical medicine and rehabilitationConcordance correlation coefficientForce platformConfidence intervalMedicineNuclear medicineBiomedical engineeringMathematicsComputer scienceStatisticsInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to evaluate a novel accelerometer-based sensor system, the Walkabout Portable Gait Monitor (WPGM), for use in kinetic gait analysis of dogs. The accelerometer was compared to the common reference standard of force platform analysis. Fifteen client-owned, orthopedically sound dogs of various breeds underwent simultaneous force platform and accelerometer gait trials to measure peak vertical forces (PVFs). The agreement between PVF for the accelerometer and force platform was measured using concordance correlation coefficient (CCC) and was found, overall, to be moderate [CCC = 0.51; 95% confidence interval (CI): 0.46 to 0.56]. The agreement between PVF for the accelerometer and force platform for the forelimbs was positive and substantial (CCC = 0.79; 95% CI: 0.74 to 0.84) and for the hind limbs was positive and low (CCC = 0.34; 95% CI: 0.29 to 0.38). As measured by the accelerometer, PVF was systematically higher than as measured by the force platform (forelimbs 55.3 N, hind limbs 144.3 N). It was also found that, when positioned over the lumbar spine, the WPGM cannot measure PVF of the individual forelimbs and hind limbs, which limits its use as a clinical tool to measure kinetic variables in 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.201
GPT teacher head0.349
Teacher spread0.148 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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