Herbal medicine (Danggui-Shaoyao-San) and Ear Acupoint Pressing Beans in the treatment of dysmenorrhea caused by endometriosis and adenomyosis: a study protocol randomized controlled trial
Bibliographic record
Abstract
Abstract Endometriosis and adenomyosis are two of the most common causes of secondary dysmenorrhea and often lead to a deterioration in the quality of life. Traditional Chinese medicine and acupuncture are widely used in the treatment of menstrual pain in clinical practice. Danggui Shaoyao San (DSS) and ear acupoint pressing beans may constitute an effective treatment strategy for women with dysmenorrhea due to endometriosis and/or adenomyosis, although evidence is limited. Methods/design This randomized, controlled clinical trial aims to recruit patients who suffer from menstrual pain due to endometriosis and/or adenomyosis to evaluate the efficacy and safety of DSS and auriculotherapy. Primary outcome measures are Visual Analog Scale (VAS), Short-Form McGill Pain Questionnaire (SF-MPQ), dysmenorrhea symptoms and traditional Chinese medicine correlative time points. Discussion This pivotal trial will be a standardized, scientific, clinical trial designed to evaluate the use of DSS and auriculotherapy in the treatment of dysmenorrhea due to endometriosis and/or adenomyosis. The trial will also conform to the international standards of clinical trials for the recognition of traditional Chinese medicine. Trail registration Chinese Clinical Trail Registry, ID: ChiCTR-IOR-17013829 Registered on 11th December 2017 Keywords: Danggui Shaoyao San; Ear pressing beans; Endometriosis; Adenomyosis; Dysmenorrhea; Randomized controlled trial
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".