Post-stroke dementia – a comprehensive review
Bibliographic record
Abstract
Post-stroke dementia (PSD) or post-stroke cognitive impairment (PSCI) may affect up to one third of stroke survivors. Various definitions of PSCI and PSD have been described. We propose PSD as a label for any dementia following stroke in temporal relation. Various tools are available to screen and assess cognition, with few PSD-specific instruments. Choice will depend on purpose of assessment, with differing instruments needed for brief screening (e.g., Montreal Cognitive Assessment) or diagnostic formulation (e.g., NINDS VCI battery). A comprehensive evaluation should include assessment of pre-stroke cognition (e.g., using Informant Questionnaire for Cognitive Decline in the Elderly), mood (e.g., using Hospital Anxiety and Depression Scale), and functional consequences of cognitive impairments (e.g., using modified Rankin Scale). A large number of biomarkers for PSD, including indicators for genetic polymorphisms, biomarkers in the cerebrospinal fluid and in the serum, inflammatory mediators, and peripheral microRNA profiles have been proposed. Currently, no specific biomarkers have been proven to robustly discriminate vulnerable patients ('at risk brains') from those with better prognosis or to discriminate Alzheimer's disease dementia from PSD. Further, neuroimaging is an important diagnostic tool in PSD. The role of computerized tomography is limited to demonstrating type and location of the underlying primary lesion and indicating atrophy and severe white matter changes. Magnetic resonance imaging is the key neuroimaging modality and has high sensitivity and specificity for detecting pathological changes, including small vessel disease. Advanced multi-modal imaging includes diffusion tensor imaging for fiber tracking, by which changes in networks can be detected. Quantitative imaging of cerebral blood flow and metabolism by positron emission tomography can differentiate between vascular dementia and degenerative dementia and show the interaction between vascular and metabolic changes. Additionally, inflammatory changes after ischemia in the brain can be detected, which may play a role together with amyloid deposition in the development of PSD. Prevention of PSD can be achieved by prevention of stroke. As treatment strategies to inhibit the development and mitigate the course of PSD, lowering of blood pressure, statins, neuroprotective drugs, and anti-inflammatory agents have all been studied without convincing evidence of efficacy. Lifestyle interventions, physical activity, and cognitive training have been recently tested, but large controlled trials are still missing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".