The efficacy and safety of post-stroke cognitive impairment therapies: an umbrella review
Bibliographic record
Abstract
Background: Stroke survivors are at significantly increased risk of cognitive impairment, which affects patients’ independence of activities of daily living (ADLs), social engagement, and neurological function deficit. Many studies have been done to evaluate the efficacy and safety of post-stroke cognitive impairment (PSCI) treatment, and due to the largely inconsistent clinical data, there is a need to summarize and analyze the published clinical research data in this area. Objective: An umbrella review was performed to evaluate the efficacy and safety of PSCI therapies. Methods: Three independent authors searched for meta-analyses and systematic reviews on PubMed, the Cochrane Library, and the Web of Science to address this issue. We examined ADL and Barthel index (BI), Montreal Cognitive Assessment (MoCA), neurological function deficit as efficacy endpoints, and the incidence of adverse events as safety profiles. Results: In all, 312 studies from 19 eligible publications were included in the umbrella review. The results showed that angiotensin-converting enzyme inhibitors (ACEI) and N-methyl-D-aspartate (NMDA) antagonists, cell therapies, acupuncture, and EGB76 can improve the MoCA and ADL, and the adverse effects were mild for the treatment of PSCI. Moreover, Vinpocetine, Oxiracetam, Citicoline, thrombolytic therapy, Actovegin, DL-3-n-Butylphthalide, and Nimodipine showed adverse events or low article quality in patients with PSCI. However, the research evidence is not exact and further research is needed. Conclusion: Our study demonstrated that ACEI inhibitors (Donepezil) and NMDA antagonists (Memantine), EGB761, and acupuncture are the ADL and BI, MoCA, and neurological function deficit medication/therapy, respectively, for patients with PSCI. Clinical Trial Registration: https://inplasy.com/inplasy-2022-11-0139/ ; Identifier: INPLASY2022110139.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".