Effects of Ginkgo diterpene lactone meglumine on cognition and neurological function in stroke patients: a systematic evaluation and meta-analysis
Bibliographic record
Abstract
ABSTRACT Objectives Evaluating the effect of GDLM on cognitive function and neurological prognosis in stroke patients Methods Investigators systematically searched nine databases for all randomized controlled trials (RCTs) from the inception of the databases to February 2025. Two investigators independently conducted literature screening, data extraction, bias risk assessment, and analyzed the data using data analysis software (StataMP 18). Results We included 34 RCTs involving 3,641 patients. Compared with the control group, the GDLM group showed significantly greater improvements in Montreal Cognitive Assessment (MoCA) (weighted mean difference [WMD] = 3.05, 95% confidence interval [CI] = 1.21 to 4.90), Mini-Mental State Examination (MMSE) (WMD = 3.68, 95% CI = 2.31 to 5.06), Barthel Index (BI) (WMD = 9.47, 95% CI = 8.02 to 10.91), and Activities of Daily Living Scale (ADLs) (WMD = 8.80, 95% CI = 6.59 to 11.00) scores. Additionally, more pronounced reductions were observed in National Institutes of Health Stroke Scale (NIHSS) (WMD = −2.30, 95% CI = −2.80 to −1.80) and modified Rankin Scale (mRS) (WMD = −0.45, 95% CI = −0.67 to −0.23) scores (all P < 0.001). Conclusion GDLM improves cognitive and neurological functions and enhances activities of daily living in stroke patients, supporting its clinical application. Future studies should involve longer follow-up, larger samples, and more rigorous RCTs to further clarify its effects on stroke patients.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".