Functional and Cognitive Consequences of Silent Stroke Discovered Using Brain Magnetic Resonance Imaging in an Elderly Population
Bibliographic record
Abstract
OBJECTIVES: To evaluate the prevalence of silent stroke and its associated consequences on physical, cognitive, and emotional functioning in an elderly population. DESIGN: Population-based cross-sectional survey. SETTING: The Memory and Morbidity in Augsburg Elderly project in the Augsburg region of southern Germany. PARTICIPANTS: Two hundred sixty-seven community-dwelling persons aged 65 to 83. MEASUREMENTS: The presence of silent stroke was determined using magnetic resonance imaging brain scan and a single question asking for physician-diagnosed stroke in each participant. The health effect of silent stroke was assessed using rating scales for self-perceived health status (36-item short-form health survey), activities of daily living (ADLs) and instrumental ADLs, cognitive function, and depression (Center for Epidemiologic Studies Depression scale). RESULTS: Just fewer than 13% (12.7%) of participants were affected by silent stroke. Silent stroke was associated with a history of hypertension, heart surgery, and elevated C-reactive protein. Individuals with silent stroke showed impairments on the Mini-Mental State Examination test and in the cognitive domains of memory, procedural speed, and motor performance. CONCLUSION: The presence of silent stroke has a considerable effect on cognitive performance in those affected. Determining the presence of silent stroke using brain imaging may contribute to identifying individuals at risk for developing gradual neurological deficits.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".