Evaluating the Effectiveness of Neuroprotective Strategies in Enhancing Post-stroke Recovery: A Systematic Review of Meta-Analyses and Clinical Trials
Bibliographic record
Abstract
This systematic review evaluates the effectiveness of various neuroprotective strategies in enhancing recovery following acute ischemic stroke, focusing on interventions such as normobaric oxygen (NBO), lithium, selective serotonin reuptake inhibitors (SSRIs), and Cerebrolysin. Drawing upon data from six primary studies, including randomized controlled trials (RCTs) and meta-analyses, we assessed these therapies' impact on functional outcomes, motor recovery, and neurological improvement. Normobaric oxygen, across 12 RCTs, demonstrated limited efficacy in improving recovery outcomes or reducing mortality. Lithium, supported by animal models but with inconclusive human data, showed potential in reducing stroke volume but did not significantly enhance functional recovery in clinical trials. SSRIs, particularly fluoxetine, showed moderate success in improving motor recovery, as evidenced by the FLAME (Fluoxetine for Motor Recovery after Acute Ischaemic Stroke) trial and meta-analyses. Cerebrolysin demonstrated consistent improvement in early neurological function and motor recovery, with a number-needed-to-treat (NNT) of 7.1 for early NIHSS (National Institutes of Health Stroke Scale) score improvements. Our Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search covered PubMed, Medline, Embase, and the Cochrane Library up to September 2024. These findings emphasize the mixed efficacy of these neuroprotective interventions and underscore the necessity for personalized treatment protocols and further large-scale, controlled trials to clarify their roles in clinical practice. This review contributes to the ongoing dialogue on optimizing post-stroke recovery and highlights the critical need for evidence-based neuroprotective strategies.
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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 | 0.023 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".