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Record W2994683632 · doi:10.1111/jgs.16265

Mapping Associations Between Gait Decline and Fall Risk in Mild Cognitive Impairment

2019· article· en· W2994683632 on OpenAlexafffund
Frederico Pieruccini‐Faria, Yanina Sarquis‐Adamson, Iván Antón‐Rodrigo, Alicia Noguerón‐García, Nick W. Bray, Richard Camicioli, Susan Hunter, Mark Speechley, Bill McIlroy, Manuel Montero‐Odasso

Bibliographic record

VenueJournal of the American Geriatrics Society · 2019
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of AlbertaLondon Health Sciences CentreParkwood InstituteUniversity of WaterlooLawson Health Research InstituteWestern University
FundersInstitute of AgingCanadian Institutes of Health Research
KeywordsMedicineGaitPhysical medicine and rehabilitationConfidence intervalSTRIDEPoison controlHazard ratioFalls in older adultsCognitionCognitive declinePreferred walking speedPhysical therapyCohort studyInjury preventionInternal medicineDementiaEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: Compared to their cognitively healthy counterparts, older adults with mild cognitive impairment (MCI) exhibit higher risk of falls, specifically with injuries. We sought to determine whether fall risk in MCI is associated with decline in higher-level brain gait control. DESIGN: Longitudinal study. SETTING: Community-dwelling adults from the Gait and Brain Study Cohort. PARTICIPANTS: A total of 110 participants, aged 65 years or older, with MCI. MEASUREMENTS: Biannual assessments for medical characteristics, cognitive performance, fall incidence, and gait performance for up to 7 years. Seven spatiotemporal gait parameters, including variabilities, were recorded using a 6-meter electronic walkway. Principal components analysis was used to identify independent gait domains related to higher-level (pace and variability domains) and lower-level (rhythm domain) brain control. Associations between gait decline and incident falls were studied with Cox regression models adjusted for baseline covariates. RESULTS: Of participants enrolled, 40% experienced at least one fall (28% of them with injuries) over a mean follow-up of 31.6 ± 23.9 months. From the pace domain, slower gait speed (adjusted hazard ratio [aHR] per 10-cm/s decrease = 4.62; 95% confidence interval [CI] = 1.84-11.61; P = .001) was associated with severe injurious falls requiring emergency room (ER) visit; from the variability domain, stride time variability (aHR per 10% increase during follow-up = 2.17; 95% CI = 1.02-4.63; P = .04) was associated with higher risk of all injurious falls. Rhythm domain was not associated with fall risk. Decline in pace domain was significantly associated with falls with ER visit (aHR = 3.67; 95% CI = 1.46-9.19; P = .005). After adjustments for multiple comparisons, gait speed and pace domain remained significantly associated with falls with ER visits. No statistically significant associations were found between gait domains and overall falls (P ≥ .06). CONCLUSION: Higher risk of injurious falls in older adults with MCI is associated with decline in gait parameters related to higher-level brain control. J Am Geriatr Soc 68:576-584, 2020.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.022
GPT teacher head0.341
Teacher spread0.319 · 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

Citations55
Published2019
Admission routes2
Has abstractyes

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