Meta-analysis of Comparative Study on St. John's Wort Extract in the Treatment of Post-stroke Depression
Bibliographic record
Abstract
Objective The purpose of this systematic review with meta-analysis was to evaluate the difference of efficacy and side effect of St.John's wort extract(Neurostan) in the treatment of post-stroke depression.Methods The related reports concerning the clinical trials of St.John's wort extract(Neurostan) in treating post stroke depression published domestically and abroad between 1978 and 2011 were searched and Meta analysis was conducted on the selected reports by means of Review Manager 5.0.Results There are 11 reports matches our including standards.①Treatment effects on PSD symptoms: Compared with conventional antidepressants,the efficiency of St.John's wort extract in treatment of post stroke depression has no significant variance(P 0.05).Compared with conventional therapy,the efficiency of st.John's wort extract in the treatment of post stroke depression has significant variance(P ﹤ 0.05).②Treatment effects on neurologic impairment: Compared with conventional antidepressants,the efficiency of St.John's wort extract in treatment of post stroke depression has significant variance(P ﹤ 0.05).Compared with conventional therapy,the efficiency of St.John 's wort extract in treatment of post stroke depression has significant variance(P ﹤ 0.05).Conclusion ①Treatment effects of St.John's wort extract(Neurostan) on PSD symptoms: compared with conventional antidepressants,the efficiency is considerable.Compared with conventional therapy,the efficiency is better.②Treatment effects on neurologic impairment: compared with both conventional antidepressants and conventional therapy,the efficiency is better.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.053 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".