Acupoint Thread Embedding Combined With Wenshen Bugu Decoction for the Treatment of Aromatase Inhibitor-Associated Musculoskeletal Symptom Among Postmenopausal Breast Cancer Patients: Study Protocol of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Aromatase inhibitors (AIs) are recommended as the preferred therapy for postmenopausal women with hormone receptor-positive (HR+) breast cancer. As a result, aromatase inhibitor-associated musculoskeletal symptom (AIMSS) have become a major problem leading to therapy discontinuation and decreased quality of life in patients receiving adjuvant AIs treatment. Multiple therapies have been attempted, but have yielded limited clinical results. This study will be performed to determine whether acupoint thread embedding (ATE) combined with Wenshen Bugu Decoction can effectively treat AIMSS, so as to improve the AIs medication compliance of postmenopausal breast cancer patients. METHODS: This study will utilize a randomized, 2 parallel groups controlled trial design. A total of 128 eligible postmenopausal breast cancer women with AIMSS will be randomized to receive a 12-week treatment with Wenshen Bugu Decoction alone (control group) or in combination with ATE (treatment group) in a 1:1 ratio. The primary outcome will be the 12 week Brief Pain Inventory Worst Pain (BPI-WP) score. The secondary outcome measures will include response rate, Brief Pain Inventory-Short Form (BFI-SF), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Functional Assessment of Cancer Therapy-Endocrine Symptom (FACT-ES), Functional Assessment of Cancer Therapy-Breast (FACT-B), bone marrow density (BMD), blood markers of bone metabolite, Morisky medication adherence scale-8 (MMAS-8), credibility and expectancy, and survival outcomes. DISCUSSION: This trial may provide clinical evidence that ATE combined with Wenshen Bugu Decoction can be beneficial for treating AIMSS among postmenopausal breast cancer survivors. Our findings will be helpful to enhance the quality of life and reduce the occurrence of AIs withdrawal.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".