Abstract WP233: Unraveling the Profile and Risk Factors of Post-Stroke Cognitive Impairment Among West African Stroke Survivors: Data From the SIREN Study
Bibliographic record
Abstract
Background: There is paucity of data on the epidemiology of post-stroke cognitive impairment among African stroke survivors. The aim of this study is to report the profile and risk factors of post- stroke cognitive impairment among stroke survivors participating in the Stroke Investigative Research and Education Network (SIREN) Study. Methods: 1566 were evaluated with the Montreal Cognitive Assessment (MoCA) tool, the Community Screening Instrument for Dementia (CSID) and the Stick Design Test three months after the index stroke. Domain scores were derived for executive function, language memory and visuo-constructive/visuospatial domains. Cut off scores were derived from normative cognitive data obtained from comparable healthy stroke-free control subjects. We used conditional logistic regression to estimate odds ratios (OR) with 95% CIs. Results: Of 1566 stroke survivors [mean age 57.7 (13.4) years] who were assessed 3 months after stroke, 37% were impaired in global cognition and 18-43% were impaired in different domains of cognition (executive, memory, language and visuo-constructive). Stroke severity measured by modified NIHSS score had a strong significant negative association with cognitive function in all domains OR 1.96(1.32-2.91). Older age 1.04 (1.02-2.05), male gender 0.69 (0.50 -0.96), low intake of green leafy vegetables 2.83 (2.03 -3.95) and cardiac disease 1.86 (1.31 -2.75) were associated with poorer cognitive performance in different cognitive domains. Conclusion: The frequency of post-stroke cognitive impairment was high among African stroke survivors three months after the ictus. Diet low in green leafy vegetables is a potentially modifiable risk factor for post-stroke cognitive impairment among West Africans.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".