Post - Stroke Vascular Cognitive Impairment Among Indigenous Africans : Data from the SIREN Study
Bibliographic record
Abstract
Cognitive impairment and dementia (CID) attributable to vascular brain disease is an escalating public health problem. Stroke, a leading cause of vascular CID has the highest burden and worse outcomes (including cognitive dysfunction) among people of African ancestry. About 50% of indigenous African stroke survivors exhibited cognitive dysfunction in previous studies although the studies had modest sample sizes. Determinants of VCI include The reasons for the susceptibility to poorer cognitive outcomes likely involve genetic, epigenetic and non - genetic mechanisms. 1166 stroke survivors in the SIREN cohort underwent cognitive assessment at 3 months after stroke using the Community Screening Instrument for Dementia (CSID), the Montreal Cognitive Assessment (MoCA and selected tools from the Vascular Neuropsychological Battery (V- NB). Subjects were cognitively categorized into normal and impaired using the VasCog Criteria and covariates associated with cognitive impairment were determined by logistic regression using the R software. Among 1166 West African stroke survivors in the SIREN study, 48% were cognitively impaired at 3 months after stroke. Factors independently associated with post-stroke cognitive impairment include low educational attainment, higher stroke severity, male gender, low intake of green leafy vegetable, diabetes mellitus and older age while a higher cognitive and social lifestyle score (CSLS) was protective. Global and cortical atrophy on neuroimaging were also associated with cognitive impairment in the cohort. Frequency of post-stroke cognitive dysfunction is high among indigenous African stroke survivors and determinants include ageing, vital social determinants of health, vascular factors, dietary and imaging factors. Supported by: NIH grants NIH U54HG007479-01, U01HG010273, R01NS107900 and UK Royal Society FLAIR Grants FLR/R1/191813 and FCG/R1/201034.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".