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Markerless Vs. Marker-based Kinematics During 45-degree Cutting In Pediatric Patients

2024· article· en· W4402662274 on OpenAlexaboutno aff
Tishya A. L. Wren, Shawn M. Roberts, Avery Lee, Eva Ciccodicola

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2024
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsKinematicsDegree (music)MedicineArtificial intelligenceOrthodonticsPhysical medicine and rehabilitationComputer sciencePhysicsAcoustics

Abstract

fetched live from OpenAlex

PURPOSE: Sports biomechanical analysis has traditionally been performed using marker-based motion analysis. Markerless motion capture systems are now available, but need to be validated for clinical and research use. This study compares kinematics from the Theia3D markerless system vs. the Vicon marker-based system for cutting in pediatric patients. METHODS: 10 pediatric athletes (6 female; age range 10-18 years) with lower extremity injuries (5 ACL, 1 MCL, 4 lower limb pain, 1 unspecified knee injury) underwent sports biomechanical testing with data being captured concurrently by a traditional marker-based motion capture system (Nexus2, Vicon, Oxford, UK) and a markerless system (Theia3D, Ontario, Canada and Qualysis, Goteborg, Sweden). Participants performed a 45° cut by running straight forward, planting their left foot on a force plate, and cutting to the right along a guideline on the floor. Kinematic data from one successful trial per patient was compared between the markerless and marker-based systems using root mean square difference (RMSD), mean difference, and RMSD after subtracting the mean difference (RMSDoffset). RESULTS: Kinematics showed similar patterns between the markerless and marker-based systems. While average RMSD of the raw data was >5° for most joint angles, all RMSDoffset were < 7° except for trunk and hip rotation (9.7° and 7.5°, respectively) (Table). Systematic differences between the two systems >10° were observed in trunk tilt and ankle inversion/eversion with differences >5° in ankle rotation, knee rotation, and knee flexion/extension. CONCLUSIONS: The markerless system produced kinematic patterns similar to marker-based motion analysis during cutting, but the differences were greater than observed during gait1 and not always systematic. Additional validation of accuracy and reliability is recommended before markerless motion capture is used for clinical biomechanical sports assessment. 1 Wren 2023. Gait Pos 104, 9-14

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.302
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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