Acupuncture for menopausal hot flashes: clinical evidence update and its relevance to decision making
Bibliographic record
Abstract
OBJECTIVE: There is conflicting evidence on the efficacy and effectiveness of acupuncture for menopausal hot flashes. This article synthesizes the best available evidence for when women are considering whether acupuncture might be useful for menopausal hot flashes. METHODS: We searched electronic databases to identify randomized controlled trials and systematic reviews of acupuncture for menopausal hot flushes. RESULTS: The overall evidence demonstrates that acupuncture is effective when compared with no treatment, but not efficacious compared with sham. Methodological challenges such as the complex nature of acupuncture treatment, the physiological effects from sham, and the significant efficacy of placebo therapy generally in treating hot flashes all impact on these considerations. CONCLUSIONS: Acupuncture improves menopausal hot flashes compared with no treatment; however, not compared with sham acupuncture. This is also consistent with the evidence that a range of placebo interventions improve menopausal symptoms. As clinicians play a vital role in assisting evidence-informed decisions, we need to ensure women understand the evidence and can integrate it with personal preferences. Some women may choose acupuncture for hot flashes, a potentially disabling condition without long-term adverse health consequences. Yet, women should do so understanding the evidence, and its strengths and weaknesses, around both effective medical therapies and acupuncture. Likewise, cost to the individual and the health system needs to be considered in the context of value-based health care.
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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 | 0.031 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".