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Record W2650616383 · doi:10.3389/fnhum.2017.00353

Guidelines for Assessment of Gait and Reference Values for Spatiotemporal Gait Parameters in Older Adults: The Biomathics and Canadian Gait Consortiums Initiative

2017· article· en· W2650616383 on OpenAlexaffabout
Olivier Beauchet, Gilles Allali, Harmehr Sekhon, Joe Verghese, Sylvie Guilain, Jean‐Paul Steinmetz, Reto W. Kressig, John M. Barden, Tony Szturm, Cyrille P. Launay, Sébastien Grenier, Louis Bherer, Teresa Liu‐Ambrose, Vicky L. Chester, Michele L. Callisaya, Velandai Srikanth, Guillaume Léonard, Anne‐Marie De Cock, Ryuichi Sawa, Gustavo Duque, Richard Camicioli, Jorunn L. Helbostad

Bibliographic record

VenueFrontiers in Human Neuroscience · 2017
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of AlbertaHealth and Social Services Centre University Institute of Geriatrics of SherbrookeCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanMontreal Heart InstituteUniversity of British ColumbiaUniversity of New BrunswickUniversité de MontréalInstitut Universitaire de Gériatrie de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of ManitobaGeomechanica (Canada)McGill University Health CentreMcGill UniversityUniversity of ReginaJewish General HospitalUniversity Health Network
FundersNational Health and Medical Research CouncilNational Institute on AgingNational Institutes of HealthMinistère des Affaires Sociales et de la Santé
KeywordsGaitPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Background. Gait disorders, a highly prevalent condition in older adults, are associated with several adverse health consequences. Gait analysis allows qualitative and quantitative assessments of gait that improves the understanding of mechanisms of gait disorders and the choice of interventions. This manuscript aims 1) to give consensus guidance for clinical and spatiotemporal gait analysis based on the recorded footfalls in older adults aged 65 years and over, and 2) to provide reference values for spatiotemporal gait parameters based on the recorded footfalls in healthy older adults free of cognitive impairment and multi-morbidities. Methods. International experts working in a network of two different consortiums (i.e.; Biomathics and Canadian Gait Consortium) participated in this initiative. First, they identified items of standardized information following the usual procedure of formulation of consensus findings. Second, they merged databases including spatiotemporal gait assessments with GAITRite® system and clinical information from the “Gait, cOgnitiOn & Decline” (GOOD) initiative and the Generation 100 (Gen 100) study. Only healthy - free of cognitive impairment and multi-morbidities (i.e.; ≤3 therapeutics taken daily) - participants aged 65 and older were selected. Age, sex, body mass index, mean values and coefficients of variation (CoV) of gait parameters were used for the analyses. Results. Standardized systematic assessment of three categories of items, which were demographics and clinical information, and gait characteristics (clinical and spatiotemporal gait analysis based on the recorded footfalls), were selected for the proposed guidelines. Two complementary sets of items were distinguished: a minimal data set and a full data set. In addition, a total of 954 participants (mean age 72.8 ± 4.8 years, 45.8% women) were recruited to establish the reference values. Performance of spatiotemporal gait parameters based on the recorded footfalls declined with increasing age (mean values and CoV) and demonstrated sex differences (mean values only). Conclusions. Based on an international multicenter collaboration, we propose consensus guidelines for gait assessment and spatiotemporal gait analysis based on the recorded footfalls, and reference values for healthy older adults.

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)0.052
metaresearch head score (Gemma)0.074
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.640
Threshold uncertainty score0.725

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.074
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0190.018
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0140.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0040.003

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 head0.170
GPT teacher head0.446
Teacher spread0.277 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations203
Published2017
Admission routes2
Has abstractyes

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