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Record W4319339330 · doi:10.1016/j.yfrne.2023.101061

The impact of loneliness and social isolation on the development of cognitive decline and Alzheimer’s Disease

2023· review· en· W4319339330 on OpenAlexaff
Yi Ren, Aisouda Savadlou, Soobin Park, Paul Siska, Jonathan R. Epp, Derya Sargin

Bibliographic record

VenueFrontiers in Neuroendocrinology · 2023
Typereview
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsHotchkiss Brain InstituteAlberta Children's HospitalOntario Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsLonelinessSocial isolationDiseaseDementiaIntervention (counseling)PsychologyCognitionIsolation (microbiology)Cognitive declineAlzheimer's diseaseGerontologyClinical psychologyMedicinePsychiatryBioinformaticsPathologyBiology

Abstract

fetched live from OpenAlex

Alzheimer's Disease (AD) is the leading cause of dementia, observed at a higher incidence in women compared with men. Treatments aimed at improving pathology in AD remain ineffective to stop disease progression. This makes the detection of the early intervention strategies to reduce future disease risk extremely important. Isolation and loneliness have been identified among the major risk factors for AD. The increasing prevalence of both loneliness and AD emphasizes the urgent need to understand this association to inform treatment. Here we present a comprehensive review of both clinical and preclinical studies that investigated loneliness and social isolation as risk factors for AD. We discuss that understanding the mechanisms of how loneliness exacerbates cognitive impairment and AD with a focus on sex differences will shed the light for the underlying mechanisms regarding loneliness as a risk factor for AD and to develop effective prevention or treatment strategies.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.117
GPT teacher head0.383
Teacher spread0.266 · 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

Citations118
Published2023
Admission routes1
Has abstractyes

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