Clinical Application of Traditional Chinese Medicine Therapy for Type 2 Diabetes Mellitus: An Evidence Map
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM), a common disease with a complex etiology in the world, is an important risk factor for severe cardiovascular and cerebrovascular diseases. However, treatments of T2DM are mainly based on Western medicine, whose severe side effects make traditional Chinese medicine (TCM) therapy more appealing to patients and clinicians. The overall clinical evidence for different TCM therapies in the treatment of T2DM is still unclear. This study aimed to adopt the evidence-mapping method and integrate the evidence from various researches on this topic, to depict the whole picture of TCM therapies for T2DM. This review included searches of PubMed, Embase, Web of Science, and three major Chinese literature databases (CNKI, VIP, and Wanfang) from inception to November 18, 2021. Two independent reviewers screened the literature, extracted information, and evaluated the quality of all included studies. A systematic review was subsequently performed. In total, 47 studies were reviewed, of which 46 studies (97.9%) were from China and 1 (2.1%) was from Canada. The evidence map was conducted according to different TCM therapies, including herbs or herbal extracts, compounds, powders, decoctions, pills, external treatment, basic theories and treatment principles of TCM, proprietary Chinese medicines, and unspecified TCM integrated therapies. According to the AMSTAR-2 scoring results, 4 papers were rated as high quality, 11 were low quality, and 32 were very low quality. Outcome indicators mainly focused on FBG, HbA1c, 2-h PBG, TC, TG, LDL-C, etc. The results showed that different types of TCM treatment had different improvement effects on the outcome indicators of T2DM. More consistent benefits were observed in the improvement of FBG, HbA1c, and 2-h PBG with treatment regimens based on basic theories and treatment principles of TCM, decoctions and pills, and unspecified TCM integrated therapies. Among herbs, ginger and Coptis root showed more improvement in all outcomes. Compounds, powders, and external treatment showed relatively consistent beneficial effects on the improvement of FBG. No serious adverse events were reported. Overall, the current evidence map provided an intuitive overview of the beneficial effects of TCM therapies in the treatment of type 2 diabetes. This study can be used as a reference for the clinical application of traditional Chinese medicine in T2DM, but due to the low-quality level of the included studies, it should be treated with caution in clinical practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".