Evaluating Traditional Chinese Medicine and Herbal Products for the Treatment of Gestational Diabetes Mellitus
Bibliographic record
Abstract
Gestational diabetes mellitus is the most common metabolic disorder during pregnancy with health consequences for both lives during and after pregnancy. Studies found that many pregnant women turn to complementary and alternative medicine for health maintenance or symptom relief, such as herbal medicine and acupuncture from traditional Chinese medicine. With the growing popularity of traditional Chinese medicine, we conducted a systemic search in PubMed, Web of Science, and Embase databases on research studies that investigated traditional Chinese medicine during pregnancy. The resultant hits were further searched in relation to all diabetes mellitus. In total, we found three major herbal medicine/herbal products that were associated with glycemic control in gestational diabetes, including Zuo Gui Wan, red raspberry leaves, and Orthosiphon stamineus. We further reviewed them and their relatives in relation to type 2 diabetes mellitus and found more evidence of metabolic benefits. None of the herbal medicine and products examined reported toxicity in the experimental models. Overall, treatments of gestational diabetes by western or alternative interventions are grossly understudied. It is critical to have a standardized protocol when evaluating efficacy of herbal medicine and produce quality results for women and their health-care providers to make informed treatment decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".