Efficacy and safety of 3‐n‐butylphthalide for the treatment of cognitive impairment: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Current evidence for the efficacy of pharmacological treatment in improving cognitive function is absent. Recent studies have reported that 3-n-butylphthalide (NBP) has a positive effect on improving cognitive impairment; however, its clinical efficacy and safety is unclear. Therefore, we conducted a meta-analysis to assess its efficacy and safety for cognitive impairment. METHODS: We systematically searched the PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus databases, and two reviewers independently screened and extracted the data from included studies. We synthesized the data using the Review Manager Software version 5.3. RESULTS: We included six randomized clinical trials (RCTs), encompassing 851 patients with cognitive impairment. The results showed that NBP improved cognitive impairment. Specifically, the clinical efficacy was better than that in the control group, with better performance in improving the Mini-Mental State Examination and the Montreal Cognitive Assessment scores, while decreasing the Alzheimer's Disease Assessment Scale-Cognitive subscale and the Clinician's Interview-Based Impression of Change plus caregiver input scores. There was no significant difference in the incidence of adverse events between both groups. CONCLUSION: The NBP is effective and safe in improving cognitive impairment; however, more high-quality RCTs are needed to confirm these findings.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| 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".