Diabetic Osteoporosis: A Review of Its Traditional Chinese Medicinal Use and Clinical and Preclinical Research
Bibliographic record
Abstract
Aim. The incidence of diabetic osteoporosis (DOP) is increasing due to lack of effective management over the past few decades. This review aims to summarize traditional Chinese medicine (TCM) suitability in the pathogenesis and clinical and preclinical management of DOP. Methods. Literature sources used were from Medline (Pubmed), CNKI (China Knowledge Resource Integrated Database), and CSTJ (China Science and Technology Journal Database) online databases. For the consultation, keywords such as diabetic osteoporosis (DOP), TCM, clinical study, animal experiment, toxicity, and research progress were used in various combinations. Around 100 research papers and reviews were visited. Results. Liver‐spleen‐kidney insufficiency may result in development of DOP. 18 clinical trials are identified to use TCM compound prescriptions for management of patients with DOP. TCM herbs and their active ingredients are effective in preventing the development of DOP in streptozotocin (STZ) and alloxan as well as STZ combined with ovariectomy insulted rats. Among them, most frequently used TCM herbs in clinical trials are Radix Astragali, Radix et Rhizoma Salviae Miltiorrhizae, Radix Rehmanniae Preparata, and Herba Epimedii. Some of TCM herbs also exhibit toxicities in clinical and preclinical research. Conclusions. TCM herbs may act as the novel sources of anti‐DOP drugs by improving bone and glucolipid metabolisms. However, the pathogenesis of DOP and the material base of TCM herbs still merit further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".