Acupuncture for hot flashes
Bibliographic record
Abstract
OBJECTIVE: Hot flashes are a significant problem in women going through the menopausal transition that can substantially affect quality of life. The world of estrogen therapy has been thrown into turmoil with the recent results of the Women's Health Initiative trial report. Pursuant to a growing interest in the use of alternative therapies to alleviate menopausal symptoms and a few pilot trials that suggested that acupuncture could modestly alleviate hot flashes, a prospective, randomized, single-blind, sham-controlled clinical trial was conducted in women experiencing hot flashes. DESIGN: Participants, after being randomized to medical versus sham acupuncture, received biweekly treatments for 5 weeks after a baseline assessment week. They were then followed for an additional 7 weeks. Participants completed daily hot flash questionnaires, which formed the basis for analysis. RESULTS: A total of 103 participants were randomized to medical or sham acupuncture. At week 6 the percentage of residual hot flashes was 60% in the medical acupuncture group and 62% in the sham acupuncture group. At week 12, the percentage of residual hot flashes was 73% in the medical acupuncture group and 55% in the sham acupuncture group. Participants reported no adverse effects related to the treatments. CONCLUSIONS: The results of this study suggest that the used medical acupuncture was not any more effective for reducing hot flashes than was the chosen sham acupuncture.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.010 | 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".