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Record W4410387085 · doi:10.1101/2025.05.14.25327640

Biological mechanisms underlying the link between social adversity and cognition: A scoping review

2025· review· en· W4410387085 on OpenAlexafffund
Aileen Liang, Emma Watt, Noha Gomaa

Bibliographic record

VenuemedRxiv · 2025
Typereview
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsLink (geometry)CognitionPsychologyCognitive scienceCognitive psychologyComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Abstract Background Various studies have shown that social adversity, such as loneliness or low SES, are linked with worse cognitive outcomes, though underlying biological mechanisms remain unclear. This scoping review aims to summarize existing evidence on biological processes that may underline this association. Methods Following PRISMA-ScR guidelines, studies measuring social adversity, cognition, and at least one biological mechanism were included. Results were summarized narratively and in tabular formats. Results Thirteen studies examined links between social adversity, cognition, and biological mechanisms. Inflammation, allostatic load, genetics and genetic aging markers were identified as potential mediators. Conclusion Several studies suggest that these biological mechanisms may mediate the link between social adversity and cognitive decline. However, further research is needed to clarify these complex relationships, which are crucial for developing targeted interventions, especially for socially disadvantaged populations.

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)0.009
metaresearch head score (Gemma)0.036
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 score0.012
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.036
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0120.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
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 head0.395
GPT teacher head0.523
Teacher spread0.128 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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