The Efficacy of Pharmacological and Non-Pharmacological Approaches in Post-Stroke Cognitive Impairment: A Systematic Review
Bibliographic record
Abstract
Objective: This systematic review evaluates the effectiveness of both pharmacological and non-pharmacological interventions in treating Post-Stroke Cognitive Impairment (PSCI), which is characterized by deficits in memory, attention, language, and executive functioning. Method: The author examined randomized controlled trials (RCTs) and observational studies published over the past 10 years. The studies reviewed focused on pharmacological treatments such as cholinesterase inhibitors (donepezil, rivastigmine) and NMDA receptor antagonists (memantine), as well as non-pharmacological interventions including cognitive rehabilitation, physical exercise, and non-invasive brain stimulation like transcranial magnetic stimulation (TMS).This systematic review selected articles through a comprehensive literature search on platforms like Google Scholar, PubMed, Scopus, and Research Gate. The search focused on peer-reviewed articles, systematic reviews, RCTs, and meta-analyses addressing post-stroke cognitive impairment (PSCI) and its management through pharmacological and non-pharmacological interventions. Results: Pharmacological interventions showed significant improvement in cognitive functions and daily living skills, with additional potential anti-inflammatory effects. Non-pharmacological interventions such as cognitive rehabilitation, physical activity, and acupuncture also positively influenced cognitive performance and daily functioning. The combination of both treatment modalities provided the most promising results for cognitive recovery. Conclusion: The findings indicate that a combined pharmacological and non-pharmacological approach yields optimal outcomes in improving cognitive recovery, overall health, and quality of life in stroke survivors. Future research should focus on long-term outcomes and developing personalized treatment plans to enhance effectiveness.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".