Changes in Cognitive Function over 3 Years after First-Ever Stroke and Predictors of Cognitive Impairment and Long-Term Cognitive Stability: The Erlangen Stroke Project
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cognitive impairment (CI) is frequent after stroke, but data from population-based stroke cohorts on the natural course of CI are limited. The purpose of this study was to determine changes in cognitive status over 3 years after stroke. METHODS: Data were collected from the Erlangen Stroke Project, an ongoing population-based stroke registry. The Mini-Mental State Examination (MMSE) for assessing global cognitive function was used; CI was defined as an MMSE score <24. RESULTS: From February 1998 to January 2006, 630 patients with first-ever stroke were included. Prevalence rates of CI at 3 months, 1 and 3 years were 15, 13, and 12%. In multivariable analysis, stroke severity, i.e. Barthel index (p < 0.001), age (OR = 1.03; 95% CI = 1.00-1.05) and diabetes mellitus (OR = 2.03; 95% CI = 1.13-3.67) were associated with CI at 3 months. Recovery rate from CI at 3 months after stroke was found to be 31% over the following 3 years. Intact cognitive function rate was 71% over 3 years and inversely associated with age (OR = 0.96; 95% CI = 0.96-0.94) and stroke severity (p < 0.001). CONCLUSION: CI is frequent among stroke survivors and associated with age, stroke severity, and diabetes mellitus, but recovery occurs in approximately one third of the patients over the course of 3 years. Factors affecting intact cognitive function over time are increasing age and stroke severity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".