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Record W2729075031 · doi:10.1093/geroni/igx004.908

A LONGITUDINAL STUDY OF THE NAVIGATION PATTERNS OF DEMENTIA PATIENTS AND THEIR RELATIONSHIP TO MMSE

2017· article· en· W2729075031 on OpenAlexaff
Ashish Jith Sreejith Kumar, Chiew Tong Lau, Sophia Siu Chee Chan, Ming Ma, William D. Kearns

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsBC Research (Canada)
Fundersnot available
KeywordsDementiaCognitionContrast (vision)Longitudinal studyRepeated measures designMedicineCognitive declinePsychologyAudiologyPhysical medicine and rehabilitationStatisticsMathematicsComputer scienceInternal medicinePsychiatryArtificial intelligence

Abstract

fetched live from OpenAlex

Studies of the navigational patterns of assisted living facility residents with dementia have resulted in many insights into the progression of dementia e.g. more tortuous navigation has been associated with declining mental capability. In this pilot investigation, we found minute changes in navigational features such as speed, path-efficiency, angle-turn and, ambulation-fraction were predictive of cognitive function. In this study, navigational data of 10 subjects living in an assisted living facility were collected daily over a period of one year using an Ultra-wideband real-time location system with an accuracy of 20cm at 1Hz using a method described by Kearns et al. (2012), and compared with their cognitive status as measured by the Mini Mental State Exam. Six subjects had received clinical diagnoses of dementia with MMSE scores averaging 13.33 (SD=7.6) while the four control subjects’ MMSE averaged 18 (SD=9). We hypothesized that linear trends in the aforementioned features over a lengthy period might provide useful information concerning dementia’s progression. We employed linear contrast analysis to identify increasing and decreasing trends in the features and evaluated the change using one-way ANOVA to compare the trends within the two diagnostic groups. Two patients evidenced significant linear trends in angle-turn and path-efficiency with the maximum variability captured by angle-turn (14.7% and 11.7%). Both subjects were later found to have very low MMSE value (6 and 9 respectively). In four other residents angle-turn consistently increased over the 1-year monitoring interval suggesting that their cognitive abilities may have correspondingly deteriorated over this interval.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.042
GPT teacher head0.289
Teacher spread0.247 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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