Effect of acupuncture on aromatase inhibitor-induced arthralgia in patients with breast cancer: A meta-analysis of randomized controlled trials
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Aromatase inhibitor (AI)-induced arthralgia (AIA) is a common side effect that may lead to premature discontinuation of effective hormonal therapy in patients with breast cancer. Acupuncture may relieve joint pain in patients with AIA. We conducted a meta-analysis of randomized controlled trials (RCTs) to evaluate the effectiveness of acupuncture in pain relief in AIA. METHODS: The PubMed, Embase, Cochrane Library, and Scopus databases and the ClinicalTrials.gov registry were searched for studies published before February 2017. Individual effect sizes were standardized, and a meta-analysis was conducted to calculate the pooled effect size by using a random effect model. Pain was assessed using the Brief Pain Inventory (BPI) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at 3-4, 6-8, and 12 weeks. Secondary outcomes included disability level, upper extremity function, physical performance, and quality of life. RESULTS: Five trials involving 181 patients were reviewed. Significant pain reduction was observed after 6-8 weeks of acupuncture treatment. Patients receiving acupuncture showed a significant decrease in the BPI worst pain score (weighted mean difference [WMD]: -3.81, 95% confidence interval [CI]: -5.15 to -2.47) and the WOMAC pain score (WMD: -130.77, 95% CI: -230.31 to -31.22) after 6-8 weeks of treatment. One of the 4 trials reported 18 minor adverse events in 8 patients during 398 intervention episodes. CONCLUSION: Acupuncture is a safe and viable nonpharmacologic treatment that may relieve joint pain in patients with AIA. Additional studies involving a higher number of RCTs are warranted.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.085 | 0.020 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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