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Record W3119192091 · doi:10.33137/cpoj.v3i2.34609

EFFECT OF TRANSTIBIAL PROSTHESIS MASS ON GAIT ASYMMETRIES

2020· article· en· W3119192091 on OpenAlexvenueaboutno aff
Mayank Seth, Wei Hou, Laura Goyarts, James Galassi, Eric M. Lamberg

Bibliographic record

VenueCanadian Prosthetics & Orthotics Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
FundersStony Brook University
KeywordsGaitProsthesisPhysical medicine and rehabilitationMedicineOrthodonticsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with transtibial amputation (TTA) typically walk with an asymmetrical gait pattern, which may predispose them to secondary complications and increase risk of fall. Gait asymmetry may be influenced by prosthesis mass.
 OBJECTIVE: To explore the effects of prosthesis mass on temporal and limb loading asymmetry in people with TTA following seven days of acclimation and community use.
 METHODS: Eight individuals with transtibial amputation participated. A counterbalanced repeated measures study, involving three sessions (each one week apart) was conducted, during which three load conditions were examined: no load, light load and heavy load. The light load and heavy load conditions were achieved by adding 30% and 50% of the mass difference between legs, at a proximal location on the prosthesis. Kinematic and ground reaction force data was captured while walking one week after the added mass. Symmetry indices between the prosthetic and intact side were computed for temporal (Stance and Swing time) and limb loading measures (vertical ground reaction force Peak and Impulse).
 FINDINGS: Following seven days of acclimation, no significant differences were observed between the three mass conditions (no load, light load and heavy load) for temporal (Stance time: p=0.61; Swing time: p=0.13) and limb loading asymmetry (vertical ground reaction force Peak: p=0.95; vertical ground reaction force Impulse: p=0.55).
 CONCLUSION: Prosthesis mass increase at a proximal location did not increase temporal and limb loading asymmetry during walking in individuals with TTA. Hence, mass increase subsequent to replacing proximally located prosthesis components may not increase gait asymmetry, thereby allowing more flexibility to the clinician for component selection.
 Layman's Abstract
 People with a below the knee amputation typically have an asymmetrical walking style, i.e., they spend more time and put more body weight on their non-amputated leg. This may result in development of knee or hip osteoarthritis of the non-amputated leg, over time. Further, an asymmetrical walking style may also predispose people to a greater number of falls. It is believed that the weight of a prosthesis may influence the walking asymmetry. It is, however, unclear if changing the weight of a prothesis during routine clinical visits (for example, switching or replacing prosthesis parts) would increase walking asymmetry. To explore this, eight individuals with a below the knee amputation had two different weights added to the top half of their prosthesis. After the addition of the weight, participants went home to use the device in their communities for seven days. Subsequently, they returned to the lab to record their walking. We observed that walking with the heavier prosthesis, using either load, did not increase the amount of time spent and body weight applied by our participants on their non-amputated side. Hence, adding mass to the top half of a prosthesis may not increase walking asymmetry.
 Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/34609/26769
 How To Cite: Seth M, Hou W, Goyarts L.R, Galassi J.P, Lamberg E.M. Effect of transtibial prosthesis mass on gait asymmetries. Canadian Prosthetics & Orthotics Journal. 2020;Volume 3, Issue 2, No.5. https://doi.org/10.33137/cpoj.v3i2.34609
 Corresponding Author: Mayank Seth, PhDDelaware Limb Loss Studies Lab, University of Delaware, Newark, USA.E-mail: mseth@udel.eduORCID: https://orcid.org/0000-0003-3526-7058

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score1.000

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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.

Study designSimulation or modeling
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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Citations1
Published2020
Admission routes2
Has abstractyes

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