Randomized, blinded, placebo-controlled trial of acupuncture for the management of aromatase inhibitor-associated joint symptoms in women with early-stage breast cancer
Bibliographic record
Abstract
571 Background: Aromatase inhibitors (AIs) have become the standard of care for the treatment of postmenopausal, hormone-sensitive breast cancer (BC). However, patients receiving AIs may experience joint symptoms which can lead to early discontinuation of this effective therapy. We examined whether acupuncture improves AI-induced arthralgias in women with early stage BC. Methods: This study is a randomized, single-blinded trial of true vs sham acupuncture twice weekly for 6 weeks in postmenopuasal women with early stage BC and self-reported musculoskeletal pain related to adjuvant AI therapy. The active intervention included full body/auricular acupuncture and a joint-specific point prescription, whereas the sham arm involved superficial needle insertion at nonacupoint locations. Outcome measures included the Brief Pain Inventory-Short Form (BPI-SF), Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC), and Modified Score for the Assessment and Quantification of Chronic Rheumatoid Affections of the Hands (M-SACRAH) obtained at baseline, 3 and 6 weeks. Lower scores reflect improvement in symptoms. Results: Of 43 women enrolled, 38 were evaluable at 6 weeks. Baseline characteristics were comparable between the groups. Median age: 57 (37–77); White/Hispanic/Black/Asian: 15/21/1/1; median body mass index (kg/m2): 29 (18–45). True acupuncture was associated with about a 50% decrease in mean BPI-SF scores, whereas no change from baseline was observed for the sham acupuncture group (see table). Similar findings were seen for the WOMAC and M-SACRAH pain, stiffness, and function scores (P<0.05). No adverse events were reported. Conclusions: Women with AI-induced arthralgias treated with acupuncture had significant improvement of joint pain and stiffness, which was not seen with sham acupuncture. Acupuncture is an effective and well-tolerated strategy for managing this common treatment-related side effect. [Table: see text] No significant financial relationships to disclose.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".