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Record W4389767598 · doi:10.1684/pnv.2023.1119

Willingness to age in place and anosognosia of risks in Alzheimer’s disease

2023· article· en· W4389767598 on OpenAlexaff
Charline Compagne, Hélène Trimaille, Magalie Bonnet, Lénaïc Ferrero, Éloi Magnin, Thomas Tannou

Bibliographic record

VenueGériatrie et Psychologie Neuropsychiatrie du Vieillissement · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentres Intégré Universitaires de Santé et de Services SociauxInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsAnosognosiaDementiaDiseaseAging in placeIndependence (probability theory)PsychologyGerontologyAlzheimer's diseaseMedicineDevelopmental psychologyPsychiatryCognitionPathology

Abstract

fetched live from OpenAlex

Alzheimer's disease leads to an alteration of decision-making abilities which may increase risk-taking behaviours, particularly associated anosognosia. Anticipating the progression of the disease raises a number of questions, particularly in relation to aging in place. Our qualitative study aimed to identify the arguments used by older patients with Alzheimer's disease when choosing a place to age. The study included 22 older adults, living at home, and diagnosed as mild dementia. The patients' arguments in favour of ageing in place were based mainly on the preservation of internal security, through the familiarity of places and relations as well as the maintenance of their independence and their lifestyle habits, allowing stability in their daily lives. Despite the identification of memory loss, the associated risks were minimized or hidden from the reflection on the choice of the place to age.

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)0.004
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0010.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.070
GPT teacher head0.400
Teacher spread0.330 · 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 routes1
Has abstractyes

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