The effect of Chinese herbal medicine Jian Ling Decoction for the treatment of essential hypertension: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Jian Ling Decoction (JLD) is often prescribed to improve hypertension-related symptoms in China. However, this treatment has not been systematically reviewed for its efficacy against essential hypertension (EH). This review aims to assess the current clinical evidence of JLD in the treatment of EH. DESIGN: Seven electronic databases, including the Cochrane Central Register of Controlled Trials, PubMed, EMBASE, the Chinese National Knowledge Infrastructure (CNKI), the Chinese Scientific Journal Database (VIP), the Chinese Biomedical Literature Database (CBM) and the Wanfang Database, were searched up to March 2014. Randomised control trials (RCTs) comparing JLD or combined with antihypertensive drugs versus antihypertensive drugs were included. We assessed the methodological quality, extracted the valid data and conducted the meta-analysis according to criteria from the Cochrane group. The primary outcome was categorical or continuous blood pressure (BP), and the secondary outcome was quality of life (QOL). RESULTS: Ten trials (655 patients) with unclear-to-high risk of bias were identified. Meta-analysis showed that JLD used alone showed no BP reduction effect; however, improvement on QOL was found in the JLD group compared to antihypertensive drugs. A significant reduction in systolic and diastolic BP was observed for JLD plus antihypertensive drugs when compared with antihypertensive drugs alone. No serious adverse effects were reported. CONCLUSIONS: Owing to insufficient clinical data, it is difficult to draw a definite conclusion regarding the effectiveness and safety of JLD for EH, and better trials are needed.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".