Aromatase Inhibitors for Ovulation Induction
Why this work is in the frame
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Bibliographic record
Abstract
CONTEXT: For the last 40 yr, the first line of treatment for anovulation in infertile women has been clomiphene citrate (CC). CC is a safe, effective oral agent but is known to have relatively common antiestrogenic endometrial and cervical mucous side effects that could prevent pregnancy in the face of successful ovulation. In addition, there is a significant risk of multiple pregnancy with CC, compared with natural cycles. Because of these problems, we proposed the concept of aromatase inhibition as a new method of ovulation induction that could avoid many of the adverse effects of CC. The objective of this review was to describe the different physiological mechanisms of action for CC and aromatase inhibitors (AIs) and compare studies of efficacy for both agents for ovulation induction. EVIDENCE ACQUISITION: We conducted a systematic review of all the published studies, both controlled and noncontrolled, comparing CC and AI treatment, either alone or in combination with gonadotropins, for ovulation induction or augmentation, identified through the Entrez-PubMed search engine. EVIDENCE SYNTHESIS: Because of the recent acceptance of the concept of using AIs for ovulation induction, few controlled studies were identified, and the rest of the studies were pilot or preliminary comparisons. Based on these studies, it appears that AIs are as effective as CC in inducing ovulation, are devoid of any antiestrogenic side effects, result in lower serum estrogen concentrations, and are associated with good pregnancy rates with a lower incidence of multiple pregnancy than CC. When combined with gonadotropins for assisted reproductive technologies, AIs reduce the dose of FSH required for optimal follicle recruitment and improve the response to FSH in poor responders. CONCLUSIONS: Preliminary evidence suggests that AIs may replace CC in the future because of similar efficacy with a reduced side effect profile. Although worldwide experience with AIs for ovulation induction is increasing, at present, definitive studies in the form of randomized controlled trials comparing CC with AIs are lacking.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.001 | 0.002 |
| 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 it