Acupuncture for Treatment of Arthralgia Secondary to Aromatase Inhibitor Therapy in Women with Early Breast Cancer: Pilot Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Aromatase inhibitors (AIs) are recommended as adjuvant hormone treatment for postmenopausal women with early breast cancer. A substantial proportion of women taking AIs experience joint pain and stiffness. Studies have suggested that acupuncture may be effective in treating joint pain. OBJECTIVE: A pilot study was conducted to evaluate the feasibility, safety and efficacy of using acupuncture to treat AI-induced arthralgia. METHODS: A total of 32 patients were randomised to receive either sham or real electroacupuncture (EA) twice weekly for 6 weeks. Outcomes of joint pain, stiffness and physical function were measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), overall pain severity and interference with the BPI-SF and quality of life (QOL) with the Functional Assessment of Cancer Therapy-General (FACT-G) instrument. Hand strength was assessed by a grip test, and a serum marker of inflammation (C reactive protein (CRP)) was also measured. All assessments were performed at baseline, 6 weeks and 12 weeks, except for blood samples at baseline and 6 weeks only. RESULTS: No serious adverse events were reported during or after acupuncture treatments. There were no significant differences in outcome measures. However, positive trends were observed in stiffness and physical function at week 12 in favour of real EA. CONCLUSIONS: Findings suggest that acupuncture is feasible and safe in patients with breast cancer with joint pain caused by AI. A larger study with adequately powered to confirm these results and detect clinically relevant effects is needed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it