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Record W4390201314 · doi:10.1002/alz.076293

Prevalence of mild cognitive impairment in an urban, community dwelling, elderly population and its association with Serum Vitamin B12 levels in South India.

2023· article· en· W4390201314 on OpenAlexaboutno aff
Jayakumar Menon, Suvarna Jyothi Kantipudi

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVitamin B12CognitionPopulationCognitive impairmentDiseaseGerontologyMontreal Cognitive AssessmentEnvironmental healthInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Prevalence of cognitive disorders is becoming higher in low and middle income societies with increasing lifespan1. Multiple factors contribute to emergence of cognitive disorders including genes, lifestyle, diet and nutrition, co‐morbid metabolic and vascular risk factors 2. There has been only a few studies from India, looking at the prevalence of cognitive impairment in the community. Ageing and dietary preferences pose a higher risk for B12 and Folic acid deficiencies, contributing to cognitive difficulties3. Our study aims to look at the prevalence of cognitive impairment in a community dwelling elderly population and the association of mild cognitive Impairment (MCI) with serum B12 levels. Method A total of 308 individuals above the age of 60 were screened for medical issues and psychiatric illnesses. Those who fulfilled the intake criteria were evaluated using Subjective Memory Complaint Questionnaire (SMCQ), MOCA, ACE‐R or RUDAS and FAB. Serum B12 levels were evaluated for all the individuals. Result 51.8 percent of individuals had normal cognitive function. The prevalence of cognitive impairment was 48.8%. Among these 42.3% of individuals had mild cognitive impairment. There was no significant correlation between MCI and Serum B12 levels in the screened population. Conclusion There is a high prevalence of cognitive difficulties in the studied population.Dietary deficiency of Vitamin B12 does not appear to be a major factor contributing to the MCI. Alzheimer’s disease, vascular cognitive impairment and lifestyle and metabolic risk factors may need to be considered as contributory factors References: 1) Ravindranath V, Sundarakumar JS. Changing demography and the challenge of dementia in India. Nat Rev Neurol. 2021 Dec;17(12):747‐58. 2)Scheltens P et al. Alzheimer’s disease. The Lancet. 2021 Apr 24;397(10284):1577‐90. 3) Zhang C et al; Vitamin B12, B6, or Folate and Cognitive Function in Community‐Dwelling Older Adults: A Systematic Review and Meta‐Analysis. Journal of Alzheimer’s Disease. 2020 Jan 1;77(2):781‐94.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.323
Teacher spread0.279 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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