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Record W4309160266 · doi:10.1123/jab.2022-0054

Sex Differences in Spatiotemporal Gait Parameters of Transtibial Amputees

2022· article· en· W4309160266 on OpenAlexaff
Tess M. R. Carswell, Brenton Hordacre, Marc Klimstra, Joshua W. Giles

Bibliographic record

VenueJournal of Applied Biomechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGaitSTRIDEPhysical medicine and rehabilitationLower limbMetric (unit)Physical therapyMedicineRehabilitationPsychologySurgeryEngineering

Abstract

fetched live from OpenAlex

Research addressing lower limb amputee gait and prosthetic design often focuses on men, despite female lower limb amputees having different risk factors and lower success with their prosthetics overall. It is widely agreed that sex differences exist in able-bodied gait, but research analyzing sex differences in amputee gait is rare. This study compared male and female transtibial amputee gait to ascertain potential sex differences. Forty-five transtibial amputees were asked to walk at their self-selected speed, and spatiotemporal gait data were obtained. Both the mean and variability metric of parameters were analyzed for 10 male and 10 female participants. For all participants, amputated limbs had a shorter stance time, longer swing time, and larger step length. Females had a 10% shorter stance time and 26% larger normalized step and stride length than males. Female participants also walked over 20% faster than male participants. Finally, significant interactions were found in the mean and variability metric of stride velocity, indicating greater variability in women. These findings suggest that sex differences exist in transtibial amputee gait, offering possible explanations for the different comorbidities experienced by female lower limb amputees. These results have major implications for female amputees and for sex-specific research, rehabilitation, and prosthetic design.

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 score0.180
Threshold uncertainty score0.314

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 head0.011
GPT teacher head0.191
Teacher spread0.180 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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