Acupuncture for ovulation induction in polycystic ovary syndrome: a randomized controlled trial
Bibliographic record
Abstract
Acupuncture has been demonstrated to improve menstrual frequency and to decrease circulating testosterone in women with polycystic ovary syndrome (PCOS). Our aim was to investigate whether acupuncture affects ovulation frequency and to understand the underlying mechanisms of any such effect by analyzing LH and sex steroid secretion in women with PCOS. This prospective, randomized, controlled clinical trial was conducted between June 2009 and September 2010. Thirty-two women with PCOS were randomized to receive either acupuncture with manual and low-frequency electrical stimulation or to meetings with a physical therapist twice a week for 10-13 wk. Main outcome measures were changes in LH secretion patterns from baseline to after 10-13 wk of treatment and ovulation frequency during the treatment period. Secondary outcomes were changes in the secretion of sex steroids, anti-Müllerian hormone, inhibin B, and serum cortisol. Ovulation frequency during treatment was higher in the acupuncture group than in the control group. After 10-13 wk of intervention, circulating levels of estrone, estrone sulfate, estradiol, dehydroepiandrosterone, dehydroepiandrosterone sulfate, androstenedione, testosterone, free testosterone, dihydrotestosterone, androsterone glucuronide, androstane-3α,17β-diol-3-glucuronide, and androstane-3α,17β-diol-17-glucuronide decreased within the acupuncture group and were significantly lower than in the control group for all of these except androstenedione. We conclude that repeated acupuncture treatments resulted in higher ovulation frequency in lean/overweight women with PCOS and were more effective than just meeting with the therapist. Ovarian and adrenal sex steroid serum levels were reduced with no effect on LH secretion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".