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Record W4390081883 · doi:10.1093/geroni/igad104.1894

FACTORS ASSOCIATED WITH WANDERING AMONG PERSONS WITH DEMENTIA: A RETROSPECTIVE STUDY

2023· article· en· W4390081883 on OpenAlexaffabout
Antonio Miguel Cruz, Hector Perez, Emily Rutledge, Christine Daum, Lili Liu

Bibliographic record

VenueInnovation in Aging · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of WaterlooUniversity of Alberta
Fundersnot available
KeywordsDementiaMedicineDemographyLogistic regressionEthnic groupPopulationRetrospective cohort studyObservational studyGerontologyInternal medicineEnvironmental healthDiseaseSociology

Abstract

fetched live from OpenAlex

Abstract Persons living with dementia are at increased risk of getting lost and going missing due to critical wandering. Risk factors associated with critical wandering within this population are underexplored, thus prompting this study. In this retrospective observational study, we examined anonymized data from 25,785 MedicAlert® subscribers. We used a logistic regression model with a self-reported critical wandering incident as the outcome variable (p< 0.05). The average age of our sample was 75.42 (SD 14.34). Fifty-one percent (13,064/25,785) of cases had dementia and almost 22% (5,561/25,785) were involved in a critical wandering incident at least once. People living with dementia were two and a half times more likely to be involved in a missing incident compared to people without dementia (OR=2.56, 95% CI [2.39, 2.73], p< 0.001). The likelihood of being involved in a lost incident increased with advancing age; people 95-104 years old were seven times more likely to wander than those under age 65 (OR=7.11, 95% CI [5.96, 8.47], p< 0.001). Sex at birth, official Canadian languages spoken, ethnic background, population density, living arrangement, and medication were associated with increased dementia-related missing incidents. Numerous risk factors for missing incidents were identified. Our study paves the way for implementing preventative strategies to ultimately decrease the risk of going missing for person living 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 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)0.001
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 score0.019
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.049
GPT teacher head0.329
Teacher spread0.281 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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