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Record W3048186469 · doi:10.1249/jsr.0000000000000736

A Scoping Review of Multiple-modality Exercise and Cognition in Older Adults: Limitations and Future Directions

2020· review· en· W3048186469 on OpenAlexaff
Nárlon Cássio Boa Sorte Silva, Dawn P. Gill, Robert J. Petrella

Bibliographic record

VenueCurrent Sports Medicine Reports · 2020
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British ColumbiaCentre for Family MedicineWestern University
Fundersnot available
KeywordsCognitionDementiaBrain Structure and FunctionMedicineNeuroimagingConfoundingClinical psychologyGerontologyPhysical medicine and rehabilitationPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The effects of multiple-modality exercise (MME) on brain health warrants further elucidation. Our objectives were to report and discuss the current evidence regarding the influence of MME on cognition and neuroimaging outcomes in older adults without dementia. We searched the literature for studies investigating the effects of MME on measures of cognition, brain structure, and function in individuals 55 years or older without dementia. We include 33 eligible studies. Our findings suggested that MME improved global cognition, executive functioning, processing speed, and memory. MME also improved white and gray matter and hippocampal volumes. These findings were evident largely when compared with no-treatment control groups but not when compared with active (e.g., health education) or competing treatment groups (e.g., cognitive training). MME may improve brain health in older adults without dementia; however, because of possible confounding factors, more research 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.

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)0.018
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.066
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0120.013
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.098
GPT teacher head0.414
Teacher spread0.315 · 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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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