Treatment of Shengqingtongqiao Decoction for mild cognitive impairment of white matter lesions: Study protocol for a randomized, double-blind, double-dummy, parallel controlled trial
Bibliographic record
Abstract
Abstract Background White matter lesions(WML) is an important cause of mild cognitive impairment(MCI). Ginkgo biloba extracts (GBTs) are widely used to treat cognitive dysfunctions. But the treatment of MCI is still limited. Shenqingtongqiao Decoction(SQTQD), as a clinical empirical formula, has received good feedback in treating MCI of WML. However, there was a lack of solid clinical research on SQTQD in treating MCI. The purpose of this study is to evaluate the efficacy of SQTQD in the MCI patients of WML. Methods This is a randomized, double-blind, double-dummy, parallel-controlled trial. 80 participants will be assigned to receive SQTQD granules plus GBTs mimetics or SQTQD mimetic granules plus GBTs in a 1:1 ratio. The trial will last 24 weeks, including a 12-week intervention and 12-week follow-up. The primary outcome is MoCA and AVLT. The secondary outcome is a neuropsychological battery (including MMSE, SCWT, TMT, DST, SDMT, BNT, VFT, and CDT), quality of life(BI, ADL, and FAQ scores), emotion assessment(PHQ-9, GAD-7 score), Fazekas and ARWMCs scale, and fMRI. Researchers will record any adverse events throughout the trial. Discussion This study will provide evidence to evaluate the efficacy and safety of SQTQD for MCI of WML compared with GBTs. The trial is registered at Chinese Clinical Trial Registry Chinese Clinical Trial Registry (Number: ChiCTR2300068552)
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".