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Record W4294218300 · doi:10.1515/cdbme-2022-1054

Clinical utility of gait stability measures: Selection and preliminary evaluation of the margin of stability

2022· article· en· W4294218300 on OpenAlexaff
Jeremy C. Hall, Brad Roberts, Hosein Bahari, Juan Forero, Hossein Rouhani, Jacqueline S. Hebert, Albert H. Vette

Bibliographic record

VenueCurrent Directions in Biomedical Engineering · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGaitPhysical medicine and rehabilitationHeelMargin (machine learning)Gait analysisPhysical therapyMedicineGround reaction forceComputer scienceMachine learning

Abstract

fetched live from OpenAlex

Abstract Introduction: gait-related falls account for most falls in the elderly. The identification of individuals at risk of falling due to unstable gait requires clinically feasible measures that can detect impairments in gait. Our obiective was to : (1) assess the feasibility for clinical implementation of existing gait stability measures and selected measures to detect gait impairments. Methods: Nine measures were assessed for clinical feasibility, with only the margin of stability(MOS) being selected for evaluation. Ground reactions and motion of the lower body were recorded in fifteen non-disabled and three disabled participants during treadmil walking. Media-lateral MOS was calculated and compared between the non-disabled and disabled participants to evaluate its ability to detect gait impairments. Results: MOS values at heel strike(HS) and at the point between HS and contralateral toe-off with minimum MOS deviated between participants with lower limb impaiments and non-disabled participants. Conclusion: MOS at HS demonstrated the greatest potential to assess fall risk. Additional work is required before MOS can be recommanded for clinical use.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.163
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.077
GPT teacher head0.345
Teacher spread0.268 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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