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Record W4283708855 · doi:10.1101/2022.06.27.22276778

The influence of transtibial prosthesis type on gait adaptation: a case study

2022· preprint· en· W4283708855 on OpenAlexaff
Yosra Cherni, Simon Laurendeau, Maxime Robet, Katia Turcot

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsGaitProsthesisAnklePhysical medicine and rehabilitationGait analysisBiomechanicsKinematicsMedicineOrthodonticsPhysical therapySurgeryAnatomyPhysics

Abstract

fetched live from OpenAlex

ABSTRACT Purpose Gait parameters are altered and asymmetrical in individual with transtibial amputation. The purpose of this study was to evaluate and compare the effect of four different prosthetic feet on lower-limb biomechanics during gait. Methods One young adult with transtibial ampution performed four gait analysis sessions with four foot-ankle prosthesis (Variflex, Meridium, Echelon, Kinterra). Kinematic, kinetic parameters and gait symmetry were analyzed during different prosthesis conditions. Results The type of prosthesis had little effect on amputee’ spatiotemporal parameters. Throughout the stance phase, an increase hip angle and a reduced knee flexion and ankle dorsiflexion were observed in the amputated leg. For kinetic parameters, a reduced propulsive force (SI=0.42–0.65), reduced knee extension moment (mainly during Echelon and Kinterra conditions, SI=0.17 and 0.32, respectively) and an increased knee abduction moment (mainly during the Variflex and Meridium, SI=5.74 and 8.93, respectively) in the amputated leg. Lower support moments were observed in the amputated leg compared to the unaffected leg, regardless of the type of prosthesis (SI=0.61–0.80). Conclusions The prostheses tested induced different lower-limb mechanical adaptations. If better gait symmetry between lower limbs is one of the clinical goals, an objective gait analysis could help clinicians to prescribe prosthetic feet based on quantitative measurement indicators.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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 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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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