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Record W7132999492

Upper and Lower Body Intersegmental Coordination During Unsupervised Gait of Older Adults with Dementia

2023· dissertation· W7132999492 on OpenAlexafffund
Lina Musa

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsToronto Rehabilitation Institute
FundersToronto Rehabilitation Institute
KeywordsGaitTrunkDementiaBalance (ability)Lower limbThighDisplacement (psychology)Gait cycleGait analysis
DOInot available

Abstract

fetched live from OpenAlex

People with dementia are at a high risk of falls, and changes in coordination of the upper and lower body segments may contribute to this risk. Using data obtained from a vision-based gait monitoring system, the temporal cross correlation between trunk and thigh displacement (right and left) in the mediolateral direction, expressed as a percent of the gait cycle (%lag time), was measured in 52 people with dementia. Participants with dementia who fell during the study had more negative %lag times, representing a pattern of gait coordination where trunk motion lagged behind thigh motion. Negative %lag times were associated with number of falls during the study and a history of falls. Negative %lag times were also associated with older age, lower balance test scores, increased agitation scores and spatiotemporal gait measures reflecting decreased stability. Negative %lag times are a marker for impaired stability and fall risk in people with dementia.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

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 head0.012
GPT teacher head0.335
Teacher spread0.323 · 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
Published2023
Admission routes2
Has abstractyes

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