Effects of the traditional Chinese medicine Yi Shen Jian Gu granules on aromatase inhibitor-associated musculoskeletal symptoms: a study protocol for a multicenter, randomized, controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Aromatase inhibitors (AIs) are widely used as an adjuvant endocrine treatment in postmenopausal women with early-stage breast cancer. One of the main adverse effects of AIs is musculoskeletal symptoms, which leads to a lower quality of life and poor adherence to AI treatment. To date, no effective management of aromatase inhibitor-associated musculoskeletal symptoms (AIMSS) has been developed. METHODS/DESIGN: To determine whether the traditional Chinese medicine Yi Shen Jian Gu granules could effectively manage AIMSS we will conduct a multicenter, randomized, double-blind, placebo-controlled clinical trial. Patients experiencing musculoskeletal symptoms after taking AIs will be enrolled and treated with traditional Chinese medicine or placebo for 12 weeks. The primary outcome measures include Brief Pain Inventory-Short Form, Western Ontario and McMaster Universities Osteoarthritis Index, and Modified Score for the Assessment and Quantification of Chronic Rheumatoid Affections of the Hands, which will be obtained at baseline and at 4, 8, 12 and 24 weeks. DISCUSSION: The results of this study will provide a new strategy to help relieve AIMSS. TRIAL REGISTRATION ISCTN: ISRCTN06129599 (assigned 14 August 2013).
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".