Tianma Gouteng Yin as Adjunctive Treatment for Essential Hypertension: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
Background. Tianma Gouteng Yin (TGY) is widely used for essential hypertension (EH) as adjunctive treatment. Many randomized clinical trials (RCTs) of TGY for EH have been published. However, it has not been evaluated to justify their clinical use and recommendation based on TCM zheng classification. Objectives. To assess the current clinical evidence of TGY as adjunctive treatment for EH with liver yang hyperactivity syndrome (LYHS) and liver-kidney yin deficiency syndrome (LKYDS). Search Strategy. 7 electronic databases were searched until November 20, 2012. Inclusion Criteria. RCTs testing TGY combined with antihypertensive drugs versus antihypertensive drugs were included. Data Extraction and Analyses. Study selection, data extraction, quality assessment, and data analyses were conducted according to the Cochrane standards. Results. 22 RCTs were included. Methodological quality was generally low. Except diuretics treatment group, blood pressure was improved in the other 5 subgroups; zheng was improved in angiotensin converting enzyme inhibitors (ACEIs), calcium channel blockers (CCBs), and "CCB + ACEI" treatment groups. The safety of TGY is still uncertain. Conclusions. No confirmed conclusion about the effectiveness and safety of TGY as adjunctive treatment for EH with LYHS and LKYDS could be made. More rigorous trials are needed to confirm the results.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| 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) | 0.007 | 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".