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Record W2746494860 · doi:10.1111/jnc.14153

The role of exercise in mitigating subcortical ischemic vascular cognitive impairment

2017· review· en· W2746494860 on OpenAlexafffund
Elizabeth Dao, Ging‐Yuek Robin Hsiung

Bibliographic record

VenueJournal of Neurochemistry · 2017
Typereview
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsVancouver Coastal HealthVancouver Coastal Health Research InstituteUniversity of British ColumbiaUniversity of British Columbia Hospital
FundersHeart and Stroke Foundation of Canada
KeywordsVascular dementiaDementiaMedicineCognitionCognitive impairmentNeurosciencePsychologyPhysical medicine and rehabilitationIntensive care medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

Subcortical ischemic vascular cognitive impairment (SIVCI) is the most preventable form of cognitive dysfunction. There is converging evidence from animal and human studies that indicate vascular injury as the primary cause of SIVCI. Currently, there are no curative pharmaceutical treatments for vascular dementia; however, exercise may be a promising strategy to combat SIVCI. This review will focus on the role of exercise as a strategy to prevent or slow the progression of SIVCI, with particular emphasis on the mechanisms by which exercise may improve cerebrovascular function. We propose that exercise may be an effective strategy to combat SIVCI by improving cognitive function, increasing the bioavailability of neurotrophins, stimulating endothelial function, and controlling vascular risk factors. This article is part of the Special Issue "Vascular Dementia".

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.051
GPT teacher head0.336
Teacher spread0.285 · 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 designNot applicable
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

Citations27
Published2017
Admission routes2
Has abstractyes

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