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Record W4210366077 · doi:10.29271/jcpsp.2022.02.202

Association between Insulin Resistance and Cognitive Impairment

2022· review· en· W4210366077 on OpenAlexaboutno aff
Mingyue Chen, Min Zhang, Shenglin Wang, Xiaomi Ding, Yujun Lee, Guohui Jiang

Bibliographic record

VenueJournal of College of Physicians And Surgeons Pakistan · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
FundersDepartment of Science and Technology of Sichuan ProvinceNational Natural Science Foundation of China
KeywordsInsulin resistanceCochrane LibraryCognitionMeta-analysisInsulinMedicineMEDLINEAssociation (psychology)Cognitive impairmentConfidence intervalInternal medicinePsychologyPsychiatryBiologyPsychotherapistBiochemistry

Abstract

fetched live from OpenAlex

The objective of the review was to assess the relationship between insulin resistance and cognitive impairment. Medline, Embase, Web of Science and Cochrane Library were searched. Two independent authors selected studies and extracted data. Quality of included studies was assessed by NOS (Newcastle-Ottawa quality assessment scale). A random-effects model with its 95% confidence intervals (CIs) was considered for meta-analysis. Eight articles including 1,399 subjects were included in this meta-analysis. The article showed a negative association between insulin resistance and cognition (R = - 0.262; 95% CI-0.309, - 0.215). There is evidence that insulin resistance may be a mechanism of cognitive impairment. Key Words: Insulin resistance, Insulin, Cognition, Cognitive impairment, Systematic review, Alzheimer's disease.

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)0.016
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 score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.008
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.318
Teacher spread0.283 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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