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Record W4389892652 · doi:10.1249/esm.0000000000000019

The Principles of Exercise Prescription for Brain Health in Aging

2023· article· en· W4389892652 on OpenAlexaff
Jennifer J. Heisz, Emma E. Waddington

Bibliographic record

VenueExercise Sport and Movement · 2023
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDementiaMedical prescriptionExercise prescriptionMedicineDiseasePsychological interventionIntervention (counseling)GerontologyPopulation ageingPhysical medicine and rehabilitationCognitionPhysical therapyAlzheimer's diseasePopulationPsychiatryNursing

Abstract

fetched live from OpenAlex

ABSTRACT Alzheimer’s disease and related dementias are among the world’s greatest health challenges. As the population ages, global dementia rates are rising, and with no imminent cure, there is an urgent need for interventions that reduce the risk of dementia in healthy older adults. Exercise is a promising intervention; however, exercise prescriptions for optimizing brain health are lacking. This may undermine the perceived clinical utility of exercise and pose a barrier that prevents practitioners from prescribing exercise for brain health in primary care settings. This graphical review briefly summarizes the prominent neural changes in healthy aging versus Alzheimer’s disease that exercise counteracts and provides evidence-informed principles for prescribing exercise to improve cognition as a reference point for formulating personalizable prescriptions.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.002

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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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 designTheoretical or conceptual
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

Citations9
Published2023
Admission routes1
Has abstractyes

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