Chinese Herbal Medicine Qi Ju Di Huang Wan for the Treatment of Essential Hypertension: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
Background. Chinese herbs are potentially effective for hypertension. Qi Ju Di Huang Wan (QJDHW) is a commonly used Chinese herbal medicine as a monotherapy or in combination with other antihypertensive agents for the treatment of essential hypertension (EH). However, there is no critically appraised evidence such as systematic reviews or meta-analyses on the effectiveness and safety of QJDHW for EH. Methods and Findings. CENTRAL, PubMed, CBM, CNKI, VIP, and online clinical trial registry websites were searched for published and unpublished randomized controlled trials (RCTs) of QJDHW for essential hypertension up to January 2013 with no language restrictions. A total of 10 randomized trials involving 1024 patients were included. Meta-analysis showed that QJDHW combined with antihypertensive drugs was more effective in lowering blood pressure and improving TCM syndrome for the treatment of essential hypertension than antihypertensive drugs used alone. No trials reported severe adverse events related to QJDHW. Conclusions. Our review suggests that QJDHW combined with antihypertensive drugs might be an effective treatment for lowering blood pressure and improving symptoms in patients with essential hypertension. However, the finding should be interpreted with caution because of the poor methodological quality of included trials. There is an urgent need for well-designed, long-term studies to assess the effectiveness of QJDHW in the treatment of essential hypertension.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".