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Record W4402288420 · doi:10.5770/cgj.27.707

Exercise and Language Performance in Healthy Aging, Stroke and Neurodegenerative Conditions: a Scoping Review

2024· review· en· W4402288420 on OpenAlexafffundvenue
Roya Khalili, Eva Kehayia, Marc Roig

Bibliographic record

VenueCanadian Geriatrics Journal · 2024
Typereview
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in RehabilitationJewish Rehabilitation Hospital
FundersSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsMedicineStroke (engine)Physical medicine and rehabilitationGerontologyPhysical therapy

Abstract

fetched live from OpenAlex

Background: While the benefits of exercise on cognitive functions have already been reviewed, little is known about the impact of exercise on language performance. This scoping review was conducted to identify existing evidence on exercise-induced changes in language performance in healthy aging individuals and adults with stroke or neurodegenerative conditions. Methods & Results: Using the Arksey and O'Malley framework, 29 studies were included. Eleven studies in healthy aging indicated enhanced language performance, with 72.72% having significant improvement in semantic/phonological Verbal Fluency (VF) following exercise. Among 18 studies on older adults with stroke or neurodegenerative conditions, 11 reported better language performance, with 44.44% having significant improvement in picture naming/description and semantic/phonological VF by exercise. The seven remaining studies reported no significant change in language performance in persons with stroke or neurodegenerative conditions. Conclusion: Overall, exercise interventions showed improvement in language performance in healthy aging, while selective enhancement was shown for language performance in persons with either stroke or neurodegenerative conditions.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.048
GPT teacher head0.358
Teacher spread0.310 · 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 designOther design
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

Citations2
Published2024
Admission routes3
Has abstractyes

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