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Record W3016650864 · doi:10.2147/cia.s242113

<p>Spaced Retrieval and Episodic Memory Training in Alzheimer’s Disease</p>

2020· article· en· W3016650864 on OpenAlex
Jeff Small, Diana Cochrane

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Interventions in Aging · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRecallEpisodic memoryMedicineDiseaseIntervention (counseling)Cognitive psychologyPsychologyCognitionPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: This study replicated and extended the findings from the author's previous pilot study to further explore how a spaced retrieval (SR) memory training program might be effectively applied to help persons with Alzheimer's disease (AD) improve both short- and long-term recall of recent episodic events. METHODS: A quasi-experimental within-subject group study was conducted with 15 participants with a diagnosis of AD. RESULTS: Compared to a control condition, all participants were able to spontaneously recall significantly more specific details about trained events, and their recall was significantly enhanced when they were provided with cues. Although the findings indicated that people with AD were able to encode information during training, recall gains diminished by the end of the maintenance period. DISCUSSION: This study provides evidence that individuals with mild to moderate AD can learn and recall new episodic information through SR training. These findings support the use of SR as an intervention tool to help individuals maintain their functioning in episodic recent memory. However, more research into maintaining the long-term recall of recent episodic events is warranted.

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.

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)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.483
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.278
GPT teacher head0.431
Teacher spread0.154 · 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