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Record W4284714660 · doi:10.3390/jcm11133907

A Proposal of Cognitive Intervention in Patients with Alzheimer’s Disease through an Assembling Game: A Pilot Study

2022· article· en· W4284714660 on OpenAlexaboutno aff
Remedios Navarro-Martos, Francisco Nieto-Escámez

Bibliographic record

VenueJournal of Clinical Medicine · 2022
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntervention (counseling)CognitionDiseaseAlzheimer's diseaseGerontologyPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

(1) Background: Alzheimer's disease is an irreversible brain disease, and current treatments are aimed at fighting cognitive decline. We have explored the feasibility of a game-based intervention for people with moderate Alzheimer's disease; (2) Methods: Six participants, five women and one man, were recruited from a day center to participate in a five-week study, which included a re- and post-evaluation with the Montreal Cognitive Assessment (MoCA) and INECO Frontal Screening (IFS) tests. Three participants were assigned to the control group and three others to the experimental one. Both groups performed a play activity consisting of assembling a pie toy. Participants in the experimental group were asked to make a pie matching a sample after a time interval. Control group participants were asked to freely assemble a pie without the matching component; (3) Results: Patients were shown to be motivated during such an activity during the nine sessions. The experimental group showed a significant increase in IFS scores when comparing the post- and pre-intervention assessments. No significant differences were observed in MoCA scores; (4) The intervention created a social and emotional climate suitable to maintaining participants' satisfaction and motivation, as well as to developing executive function while promoting positive emotions.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.092
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.170
GPT teacher head0.517
Teacher spread0.347 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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