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Record W2262431505 · doi:10.1123/jab.28.5.560

Day-to-Day Reliability of Two Different Models to Quantify Knee Kinematics in Subjects With Knee Osteoarthritis and Healthy Controls

2012· article· en· W2262431505 on OpenAlexaff
Inga Krauß, Thomas Ukelo, Christoph M. Ziegler, Detlef Axmann, Stefan Grau, Thomas Horstmann, Alex Stacoff

Bibliographic record

VenueJournal of Applied Biomechanics · 2012
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsSagittal planeGaitKinematicsGait analysisPhysical medicine and rehabilitationOsteoarthritisReliability (semiconductor)PopulationBiomechanicsPhysical therapyMedicineKnee JointConsistency (knowledge bases)MathematicsOrthodonticsSurgeryAnatomyPhysics

Abstract

fetched live from OpenAlex

Results from instrumented gait analysis vary between test situations. Subject characteristics and the biomechanical model can influence the total amount of variability. The purpose of this study was to quantify reliability of gait data in general, and with respect to the applied model, and investigated population group. Reliability was compared between a functional and a predictive gait model in subjects with knee osteoarthritis and healthy controls. Day-to-day consistency for sagittal plane variables was comparable between models and population groups. Transversal plane variables relative to joint excursion showed larger inconsistency for repeated measures, even for a more sophisticated biomechanical approach. In conclusion, the presented reliability data of sagittal plane kinematics should be used for a reasonable interpretation of results derived in clinical gait analysis. Variables of the transversal plane should not be used as long as sources of error are not sufficiently minimized.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.269
Teacher spread0.248 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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