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Record W3135999362 · doi:10.1177/19714009211002778

Cerebral blood flow changes during aging process and in cognitive disorders: A review

2021· review· en· W3135999362 on OpenAlexaff
Naghmeh Mokhber, Aidin Shariatzadeh, Abolfazl Avan, Hamidreza Saber, G Babaei, Gary Chaimowitz, Mahmoud Reza Azarpazhooh

Bibliographic record

VenueThe Neuroradiology Journal · 2021
Typereview
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsMcMaster UniversityRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsCerebral blood flowCognitionNeuroscienceMedicinePsychologyCardiology

Abstract

fetched live from OpenAlex

We aimed to summarize the available evidence on cerebral blood flow (CBF) changes in normal aging and common cognitive disorders. We searched PubMed for studies on CBF changes in normal aging and cognitive disorders up to 1 January 2019. We summarized the milestones in the history of CBF assessment and reviewed the current evidence on the association between CBF and cognitive changes in normal aging, vascular cognitive impairment (VCI) and Alzheimer's disease (AD). There is promising evidence regarding the utility of CBF studies in cognition research. Age-related CBF changes could be related to a progressive neuronal loss or diminished activity and synaptic density of neurons in the brain. While a similar cause or outcome theory applies to VCI and AD, it is possible that CBF reduction might precede cognitive decline. Despite the diversity of CBF research findings, its measurement could help early detection of cognitive disorders and also understanding their underlying etiology.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.066
GPT teacher head0.350
Teacher spread0.284 · 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

Citations88
Published2021
Admission routes1
Has abstractyes

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