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Record W2186597806 · doi:10.1177/2333721415618858

Gait Speed and Variability for Usual Pace and Pedestrian Crossing Conditions in Older Adults Using the GAITRite Walkway

2015· article· en· W2186597806 on OpenAlexafffund
Kristin Brown, Heather Hanson, Flavio Firmani, Danmei Liu, Megan M. McAllister, Khalil Merali, Joseph H. Puyat, Maureen C. Ashe

Bibliographic record

VenueGerontology and Geriatric Medicine · 2015
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of British Columbia HospitalSimon Fraser UniversityUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsSchema crosswalkGaitPreferred walking speedPedestrianPhysical medicine and rehabilitationPaceMedicinePsychologyTransport engineeringGeographyEngineering

Abstract

fetched live from OpenAlex

Objectives: To determine gait characteristics of community-dwelling older adults at different speeds and during a crosswalk simulation. Methods: Twenty-two older adults completed walking trials at self-selected slow, usual, and fast paces, and at a crosswalk simulation, using the GAITRite walkway. These objective measures were complemented by self-report health and mobility questionnaires. Results: Gait speeds at self-selected slow, usual, and fast paces were 98.7 (18.1) cm/s, 140.9 (20.4) cm/s, and 174.0 (20.6) cm/s, respectively, and at simulated crosswalk conditions was 144.2 (22.3) cm/s. For usual pace, right step length variability was 2.0 (1.4) cm and step time variability was 13.6 (7.2) ms, compared with 2.4 (1.3) cm and 17.3 (9.7) ms, respectively, for crosswalk conditions. Discussion: Our sample of healthy older adults walked at a speed exceeding standards for crossing urban streets; however, in response to a crosswalk signal, participants adopted a significantly faster and more variable gait.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.089
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.063
GPT teacher head0.402
Teacher spread0.339 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

Explore more

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