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Record W4405620231 · doi:10.1186/s12877-024-05432-0

Social patterning of cognitive impairment in Colombia: evidence from the SABE 2015 study

2024· article· en· W4405620231 on OpenAlexaff
Alejandra Guerrero Barragán, Inés Elvira Gómez, Diego Iván Lucumí Cuesta

Bibliographic record

VenueBMC Geriatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsTrinity College
FundersAlzheimer's SocietyGlobal Brain Health InstituteAlzheimer's Association
KeywordsFunctional illiteracySocioeconomic statusMedicineDisadvantagedDementiaGerontologyPsychological interventionResidenceCognitionPublic healthDemographyEnvironmental healthPsychiatryPopulationDiseasePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Dementia, an increasingly critical public health concern in low and middle-income countries, is associated with lower socioeconomic status, early cognitive impairment, and elevated dementia-related mortality risk. This study seeks to estimate the prevalence of cognitive impairment, investigate its links with social indicators, and visualize social gradients across different regions in Colombia. METHODS: Secondary data analysis from the SABE 2015 survey, multinomial regression analyses, and equiplot graphs. RESULTS: A sample of 23,694 individuals 60 years or older from Colombia. Higher risks were observed among individuals with dark skin color (OR 1.27; 95%CI: 1.10 - 1.47), lower educational levels (OR 3.01; 95%CI:2.04 - 4.42) and reading illiteracy (OR 2.14; 95%CI: 1.87 - 2.46). Inequity patterns were identified by region of residence and income. DISCUSSION: This study underscores the need for targeted interventions aimed at reducing health inequities. The results highlight the higher prevalence rates of cognitive impairment among socially disadvantaged individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

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

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 head0.058
GPT teacher head0.387
Teacher spread0.329 · 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 teacher head, 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

Citations1
Published2024
Admission routes1
Has abstractyes

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