Effectiveness of Gotu Kola Extract 750 mg and 1000 mg Compared with Folic Acid 3 mg in Improving Vascular Cognitive Impairment after Stroke
Bibliographic record
Abstract
This study aimed to determine the effectiveness of gotu kola (Centella asiatica) in improving cognitive function in patients with vascular cognitive impairment (VCI). This study uses a quasi-experimental design. Subjects in this study were patients with poststroke cognitive impairment who were treated at two hospitals in Yogyakarta, Indonesia. The number of subjects was 48: 17 subjects were treated with 1000 mg/day of gotu kola extract, 17 subjects treated with 750 mg/day of gotu kola extract, and 14 subjects treated with 3 mg/day of folic acid for 6 weeks. A Montreal Cognitive Assessment-Indonesian version (MoCA-Ina) was conducted at the beginning of treatment and after 6 weeks of therapy. It was found that all trials effectively improved poststroke VCI based on MoCA-Ina scores over the course of the study. There is no significant difference in ΔMoCA-Ina (score at the 6th week of treatment - score at the beginning) mean score among the three groups, indicating that gotu kola is as effective as folic acid in improving poststroke VCI. Gotu kola was shown to be more effective than folic acid in improving memory domain. This study suggested that gotu kola extract is effective in improving cognitive function after stroke.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".