Clinical efficacy of Danzhi Xiaoyao Powder in the treatment of post-stroke depression
Bibliographic record
Abstract
BACKGROUND: Depression is a common complication after stroke and is closely related to the poor prognosis of stroke. Antidepressants are the priority drug in the treatment of post-stroke depression (PSD), but there are dependence and adverse reactions. Danzhi Xiaoyao Powder has a good effect on depression without obvious adverse reactions. At present, there is a lack of rigorous randomized controlled trials to evaluate the clinical efficacy of Danzhi Xiaoyao Powder in the treatment of PSD. METHODS: This is a prospective, randomized, double-blind, parallel controlled trial to explore the efficacy and safety of Danzhi Xiaoyao Powder in the treatment of PSD. The participants were randomly divided into treatment group and control group. The treatment group used Danzhi Xiaoyao Powder combined with escitalopram oxalate, and the control group used Danzhi Xiaoyao Powder simulant combined with citalopram oxalate. The two groups were both treated for 8 weeks and followed up for 3 months. Observational index includes: Total response rate, Hamilton depression scale, Barthel index, national institutes of health stroke scale, the modified Edinburgh-Scandinavian stroke scale, Incidence of adverse reactions. Finally, SPASS 22.0 software was used for statistical analysis of the data. DISCUSSION: This study will evaluate the clinical efficacy of Danzhi Xiaoyao Powder in the treatment of PSD. The results of this study will provide reliable evidence for the clinical use of Xiaoyao Powder in the treatment of PSD. TRIAL REGISTRATION: Open Science Framework Registration number: DOI 10.17605/OSF.IO/5V926.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".