Effect of acupuncture on aromatase inhibitor-induced arthralgia in patients with breast cancer: A meta-analysis of randomized controlled trials
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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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".