Complementary and alternative medicine for the treatment of type 2 diabetes.
Bibliographic record
Abstract
OBJECTIVE: To review clinical evidence supporting complementary and alternative medicine interventions for improving glycemic control in type 2 diabetes mellitus. QUALITY OF EVIDENCE: MEDLINE and EMBASE were searched from January 1966 to August 2008 using the term type 2 diabetes in combination with each of the following terms for specific therapies selected by the authors: cinnamon, fenugreek, gymnema, green tea, fibre, momordica, chromium, and vanadium. Only human clinical trials were selected for review. MAIN MESSAGE: Chromium reduced glycosylated hemoglobin (HbA(1c)) and fasting blood glucose (FBG) levels in a large meta-analysis. Gymnema sylvestre reduced HbA(1c) levels in 2 small open-label trials. Cinnamon improved FBG but its effects on HbA(1c) are unknown. Bitter melon had no effect in 2 small trials. Fibre had no consistent effect on HbA(1c) or FBG in 12 small trials. Green tea reduced FBG levels in 1 of 3 small trials. Fenugreek reduced FBG in 1 of 3 small trials. Vanadium reduced FBG in small, uncontrolled trials. There were no trials evaluating microvascular or macrovascular complications or other clinical end points. CONCLUSION: Chromium, and possibly gymnema, appears to improve glycemic control. Fibre, green tea, and fenugreek have other benefits but there is little evidence that they substantially improve glycemic control. Further research on bitter melon and cinnamon is warranted. There is no complementary and alternative medicine research addressing microvascular or macrovascular clinical outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".