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Record W7161781283 · doi:10.82308/54408

Measurement of Gait Kinematics in Multiple Sclerosis using a portable sensor

2021· dissertation· en· W7161781283 on OpenAlexaboutno aff
Aeshah Alosaimi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGaitMultiple sclerosisWearable computerGait analysisKinematicsQuality of life (healthcare)Expanded Disability Status Scale

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is a common cause of disability in young adults. Currently, around 93,000 Canadians are living with the disease and the prevalence is increasing in Canada and worldwide. MS course and clinical features vary from one individual to another and are based on type. Mobility limitations are reported in early MS and progress over the years with walking difficulties perceived as the most challenging sequela. The importance of walking limitations in defining progression of MS has raised a suggestion of using gait changes for earlier detection of disease progression. Although the Expanded Disability Status Scale (EDSS) - a measure of disease progression - relies heavily on walking ability, multiple studies have reported changes in gait that are not translated into changes in the EDSS score. However, long-term changes in the EDSS were predicted by earlier gait limitations such as slow walking speed. Measures of gait kinematics, that better characterize gait quality could be early indicators of MS disability and MS progression. The development of wearable sensors made the assessment of gait kinematics more accessible and holds promise for self-monitoring and self-management. The objective of this thesis is to contribute evidence as to the relevance of measures of gait kinematics to quantify disability in MS. The work on this thesis was made possible because of access to data from people with MS whose gait quality was assessed using a new wearable Heel2ToeTM sensor (PhysioBiometrics Inc.). The thesis comprises one manuscript with two objectives. The primary objective is to estimate the extent to which personal factors and functional indicators are associated with gait quality parameters (gait kinematics) – measured using the Heel2Toe sensor - among ambulatory people with MS and the association of gait quality parameters with measures of physical capacity. The secondary objective is to estimate the extent to which gait quality parameters - changed over 3 months period among people with MS participating in an exercise intervention that did not include gait-targeted treatment.Correlational analysis was used to link MS impairments of leg weakness, leg heaviness, leg power, impaired coordination, fatigue, bladder dysfunction and mood with parameters related to gait kinematics. The strongest relationships (r ≥ 0.4) were observed between measures of leg power (vertical jump) and mood with both the power and balance cycles of the gait cycle. Of physical capacity measures, gait quality parameters were most strongly associated (r ≥ 0.5) with the Six-Minute Walk Test. Over 3 months period, without any specific gait training, parameters of gait kinematics deteriorated in multiple participants, improved in others, and remained the same in few participants, but these proportions did not differ from uniform distribution. However, some of the gait parameters changed concordantly

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.219
GPT teacher head0.351
Teacher spread0.132 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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