Post-Stroke Cognitive Impairment: High Prevalence and Determining Factors in a Cohort of Mild Stroke
Bibliographic record
Abstract
BACKGROUND: Because of the aging population and a rise in the number of stroke survivors, the prevalence of post-stroke cognitive impairment (PSCI) is increasing. OBJECTIVE: To identify the factors associated with 3-month PSCI. METHODS: All consecutive stroke patients without pre-stroke dementia, mild cognitive disorders, or severe aphasia hospitalized in the Neurology Department of Dijon, University Hospital, France (November 2010 - February 2012) were included in this prospective cohort study. Demographics, vascular risk factors, and stroke data were collected. A first cognitive evaluation was performed during the hospitalization using the Mini-Mental State Exam (MMSE) and the Montreal Cognitive Assessment (MOCA). Patients assessable at 3 months were categorized as cognitively impaired if the MMSE score was ≤26/30 and MOCA <26/30 or if the neuropsychological battery confirmed PSCI when the MMSE and MOCA were discordant. Multivariable logistic models were used to determine factors associated with 3-month PSCI. RESULTS: Among the 280 patients included, 220 were assessable at 3 months. The overall frequency of 3-month PSCI was 47.3%, whereas that of dementia was 7.7%. In multivariable analyses, 3-month PSCI was associated with age, low education level, a history of diabetes mellitus, acute confusion, silent infarcts, and functional handicap at discharge. MMSE and MOCA scores during hospitalization were associated with 3-month PSCI (OR = 0.63; 95% CI: 0.54-0.74; p < 0.0001 and OR = 0.67; 95% CI: 0.59-0.76; p < 0.0001, respectively). CONCLUSION: Our study underlines the high frequency of PSCI in a cohort of mild stroke. The early cognitive diagnosis of stroke patients could be useful by helping physicians to identify those at a high risk of developing PSCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".