MétaCan
Menu
Back to cohort
Record W2778544143 · doi:10.1080/17483107.2017.1420250

Maintaining stable transfemoral amputee gait on level, sloped and simulated uneven conditions in a virtual environment

2017· article· en· W2778544143 on OpenAlex
James A. Sturk, Edward D. Lemaire, Emily H. Sinitski, Nancy Dudek, Markus Besemann, Jacqueline S. Hebert, Natalie Baddour

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of AlbertaCanadian Armed ForcesUniversity of OttawaAlberta Health ServicesOttawa Hospital
Fundersnot available
KeywordsGaitTrunkTreadmillPreferred walking speedAccelerationAmputationPhysical medicine and rehabilitationRoot mean squareSimulationComputer scienceMedicinePhysical therapyEngineeringPhysicsSurgeryBiology

Abstract

fetched live from OpenAlex

PURPOSE: Describe and quantify how people with transfemoral amputations (TFA) maintain stable gait over a variety of surfaces; including, downhill and uphill, top and bottom-cross-slopes, medial-lateral translations, rolling hills and simulated rocky surfaces. METHODS: Ten TFA and ten matched people without amputations (NA) walked in a virtual environment with level, sloped and simulated uneven surfaces on a self-paced treadmill. Stability was quantified using medial-lateral margin of stability (ML-MoS), step parameters, and gait variability (standard deviations for speed, temporal-spatial parameters, foot clearance and root-mean-square of medial-lateral trunk acceleration). RESULTS AND CONCLUSIONS: TFA and NA adapted to non-level conditions by changing their walking speed, step width, and foot clearance. Variability for most parameters increased across conditions, compared to level. TFA walked slower than NA with shorter, wider and longer duration steps (most differences related to speed). ML-MoS did not change compared to level; however, ML-MoS was greater on the prosthetic side than both intact side and NA limbs. Foot clearance and root-mean-square of medial-lateral trunk acceleration were greater on the prosthetic side than the intact side and NA limbs. This research provides a comprehensive analysis of the different adaptations made by people without amputations compared to people with transfemoral amputations over non-level conditions and establishes significant differences between slopes and simulated uneven surfaces for TFA. Implications for Rehabilitation Transfemoral amputation and no amputation groups adapted walking biomechanics when traversing non-level surfaces. Greatest temporal-spatial gait adaptations were walking speed, step width and foot clearance. Gait parameter variability typically increased from the level condition in both groups. Transfemoral amputation group walked slower than no amputation group with shorter, wider steps and longer duration steps. This was related to speed. Transfemoral amputation group had more trunk motion variability on the prosthetic side than no amputation group; could be related to prosthetic fit.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.793

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 studies0.0000.002
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 head0.014
GPT teacher head0.253
Teacher spread0.240 · 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