Acupuncture for menopausal vasomotor symptoms: study protocol for a randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Hot flushes and night sweats (vasomotor symptoms) are common menopausal symptoms, often causing distress, sleep deprivation and reduced quality of life. Although hormone replacement therapy is an effective treatment, there are concerns about serious adverse events. Non-hormonal pharmacological therapies are less effective and can also cause adverse effects. Complementary therapies, including acupuncture, are commonly used for menopausal vasomotor symptoms. While the evidence for the effectiveness of acupuncture in treating vasomotor symptoms is inconclusive, acupuncture has a low risk of adverse effects, and two small studies suggest it may be more effective than non-insertive sham acupuncture. Our objective is to assess the efficacy of needle acupuncture in improving hot flush severity and frequency in menopausal women. Our current study design is informed by methods tested in a pilot study. METHODS/DESIGN: This is a stratified, parallel, randomised sham-controlled trial with equal allocation of participants to two trial groups. We are recruiting 360 menopausal women experiencing a minimum average of seven moderate hot flushes a day over a seven-day period and who meet diagnostic criteria for the Traditional Chinese Medicine diagnosis of Kidney Yin deficiency. Exclusion criteria include breast cancer, surgical menopause, and current hormone replacement therapy use. Eligible women are randomised to receive either true needle acupuncture or sham acupuncture with non-insertive (blunt) needles for ten treatments over eight weeks. Participants are blinded to treatment allocation. Interventions are provided by Chinese medicine acupuncturists who have received specific training on trial procedures. The primary outcome measure is hot flush score, assessed using the validated Hot Flush Diary. Secondary outcome measures include health-related quality of life, anxiety and depression symptoms, credibility of the sham treatment, expectancy and beliefs about acupuncture, and adverse events. Participants will be analysed in the groups in which they were randomised using an intention-to-treat analysis strategy. DISCUSSION: Results from this trial will significantly add to the current body of evidence on the role of acupuncture for vasomotor symptoms. If found to be effective and safe, acupuncture will be a valuable additional treatment option for women who experience menopausal vasomotor symptoms. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12611000393954 11/02/2009.
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How this classification was reachedexpand
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".