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Record W4415369207 · doi:10.33137/cpoj.v8i2.46063

Within- and Between-Session Reliability of Pelvic Marker Placement and Posture in Lower-Limb Amputees

2025· article· en· W4415369207 on OpenAlexvenueaboutno aff
Alexandra Withey, Dario Cazzola, Abby Tabor, Elena Seminati

Bibliographic record

VenueCanadian Prosthetics & Orthotics Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
FundersUniversity of Bath
KeywordsReliability (semiconductor)PelvisClinical trialSelection (genetic algorithm)MEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Accurate placement of anatomical markers is essential for valid three-dimensional (3D) gait analysis, yet individuals with lower-limb amputation (LLA) pose unique challenges due to altered anatomy, prosthetic interfaces, and increased adiposity. OBJECTIVE: This study assessed within- and between-session reliability of pelvis marker placement and static posture kinematics in adults with unilateral LLA. METHODOLOGY: Fourteen adults with unilateral LLA (age: 58 ± 15 years, height: 174.6 ± 7.5 cm, body mass: 91.1 ± 27.7 kg, BMI: 29.6 ± 7.5 kg/m²; eleven transtibial, three transfemoral) participated in two sessions spaced 3–13 months apart. Reliability of marker distances and static posture kinematics were assessed using intraclass correlation coefficients (ICC) and standard error of measurement (SEM). FINDINGS: Within-session reliability of pelvis marker distances was good to excellent (ICC ≥ 0.78), whereas between-session reliability was lower (ICC as low as 0.14), particularly for posterior superior iliac spine markers. Pelvis kinematics demonstrated moderate reliability within sessions (average ICC ≈ 0.71), but trunk kinematics showed poor reliability. SEM values were low (<5°), suggesting acceptable absolute consistency despite variable ICCs, likely driven by postural changes and prosthetic factors. CONCLUSION: Findings support reliable pelvis marker placement within sessions but highlight challenges for longitudinal consistency. Multiple trial collections and standardised posture protocols are recommended to improve long-term reliability. Layman's Abstract Accurately placing small reflective markers on the body is very important for correctly measuring how people move during three-dimensional (3D) gait (walking) analysis. However, this can be more difficult in people with lower-limb amputations (LLA) because their anatomy is different, they use artificial limbs, and body shapes can vary. This study looked at how consistently these markers can be placed on the pelvis in adults with unilateral LLA. Fourteen adults participated in two sessions spaced 3–13 months apart. We assessed the consistency of the marker positions comparing them within and between sessions. We found that pelvis marker placement was quite reliable when tested in the same session, but less consistent between sessions, especially for markers placed on the back of the pelvis. The overall body posture and trunk positions also varied more between sessions. Even so, the size of the differences was small, meaning that the results were still fairly reliable to extract information about the motion. Findings support reliable pelvis marker placement within sessions but highlight challenges across longer time periods. Multiple trial collections and standardised posture guidelines are recommended to improve long-term reliability. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/46063/34419 How To Cite: Withey A, Cazzola D, Tabor A, Seminati E. Within- and between-session reliability of pelvic marker placement and posture in lower-limb amputees. Canadian Prosthetics & Orthotics Journal. 2025; Volume 8, Issue 2, No. 2. https://doi.org/10.33137/cpoj.v8i2.46063 Corresponding Author: Alexandra Withey,Affiliation: Affiliation: Department for Health, University of Bath, Bath, UK.E-Mail: anmw20@bath.ac.uk ORCID ID: https://orcid.org/0000-0001-9422-2306

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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