Abstract WP13: History Of Stroke Doubles The Risk Of Dementia: The Brain Attack Surveillance In Corpus Christi (BASIC)-Cognitive Project
Bibliographic record
Abstract
Background: We studied the association of stroke history with dementia in a population-based cohort study in Nueces County, Texas, USA, a bi-ethnic community with a large and primarily non-immigrant Mexican American population. Methods: Nueces County households were randomly identified and community-dwelling residents aged 65 years or older were recruited using door-to-door case ascertainment. Demographics, educational history, and medical history were obtained, and participants completed the Montreal Cognitive Assessment (MoCA), a screening scale that scores multiple domains of cognitive performance with aggregate scores ranging from 0 to 30 (lower scores worse). Using an inverse propensity weighting methodology, the 2015-2019 American Community Survey (ACS) data for the 65+ population of Nueces County were used to develop a population weight for each case in the analysis sample based on age, sex, race, ethnicity, and education level. A logistic regression model was developed to assess the relationship between stroke and dementia (defined as MoCA <20), controlling for age, ethnicity, education, and history of diabetes. Results: A total of 1226 participants completed MoCA screening, of whom 435 scored <20 (35.5%), and 154 (12.6%) reported a history of stroke. Stroke prevalence was higher among those with MoCA <20 than those with MoCA 20-30 (17.8% vs 10.3%; p < 0.001). Table 1 shows the final multivariable model demonstrating a 129% increase in the odds of MoCA <20 for those with stroke history. Conclusions: History of stroke was associated with more than double the odds of dementia after controlling for other factors. Aggressive stroke prevention is needed to reduce cognitive impairment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".