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Record W2807885022 · doi:10.1371/journal.pone.0199181

Effects of the unity vacuum suspension system on transtibial gait for simulated non-level surfaces

2018· article· en· W2807885022 on OpenAlexafffund
Gabrielle Thibault, Hossein Gholizadeh, Emily H. Sinitski, Natalie Baddour, Edward D. Lemaire

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of OttawaCanadian Armed ForcesOttawa Hospital
FundersMitacs
KeywordsGaitPhysical medicine and rehabilitationTreadmillKinematicsSuspension (topology)PopulationProsthesisBalance (ability)Work (physics)Gait analysisGround reaction forceMedicinePhysical therapySimulationComputer scienceSurgeryMathematicsPhysics

Abstract

fetched live from OpenAlex

Walking on various surfaces encountered in everyday life requires lower limb prosthesis users to continually adapt their movement patterns. Elevated vacuum suspension systems could improve transtibial amputee gait on non-level surfaces; however, research is lacking to guide clinical practice. Twelve transtibial amputees were fitted with the Össur sleeveless vacuum suspension system (Unity). After a one month accommodation period, the CAREN-Extended system was used to evaluate gait on a self-paced treadmill when walking with continuous perturbations (medial-lateral translations, rolling hills, simulated uneven ground) with an active or inactive vacuum suspension system. Significant differences between active and inactive vacuum conditions (p<0.05) were found for some temporal-spatial and kinematic gait parameters, but the differences were small and not considered clinically significant. Our findings suggest that potential vacuum pump failures would not immediately affect gait performance in a moderately high functioning amputee population. However, residual limb volume changes over time due to the removal of elevated vacuum may adversely affect socket fit, leading to greater gait differences and reduced quality of life.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.246

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 head0.026
GPT teacher head0.208
Teacher spread0.182 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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