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Effects of Spaced Retrieval Training on Semantic Memory in Alzheimer's Disease: A Systematic Review

2013· review· en· W1983299926 on OpenAlexafffund
Shiri Oren, Charlene Willerton, Jeff Small

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2013
Typereview
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsSemantic memoryPsychologyCognitive psychologyDiseaseAlzheimer's diseaseNatural language processingCognitionMedicineNeuroscienceComputer science

Abstract

fetched live from OpenAlex

PURPOSE: This article reports on a systematic review and meta-analysis of the effects of spaced retrieval training (SRT) on semantic memory in people with Alzheimer’s disease (AD) or related disorder. METHOD: An initial systematic database search identified 454 potential studies. After screening and de-duplication, 35 studies that used SRT with the population of interest remained. The authors used an appraisal point system to evaluate the quality of the studies. Twelve of the 35 studies met inclusion and exclusion criteria and passed the appraisal point system cutoff. The 12 studies were classified as Level I and II evidence. RESULTS: Although the 12 studies varied in terms of design, methodology, and quality, SRT was shown to have important positive effects on learning semantic information across the included studies. CONCLUSIONS: The findings indicate that SRT is an effective semantic memory training technique for people with AD, and consequently, recommendations are suggested for implementing SRT in practice settings. Continued research in this domain is also warranted to address limitations and gaps in the current body of research evidence, including variability in SRT protocols, effects of dementia severity on learning outcomes, maintenance effects, generalization, and the role of explicit and implicit learning in SRT.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.078
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.188
GPT teacher head0.442
Teacher spread0.254 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations25
Published2013
Admission routes2
Has abstractyes

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