Aromatase inhibitors for the treatment of infertility
Bibliographic record
Abstract
Ovarian stimulation during infertility treatment is used either alone or in conjunction with intrauterine insemination and assisted reproductive technologies. At the present time, the two main medications used for ovarian stimulation include an oral antioestrogen, clomiphene citrate and injectable gonadotrophins. In spite of the high ovulation rate, the use of clomiphene citrate is associated with adverse side effects and low pregnancy rates. In clomiphene citrate failures, gonadotrophin injections are generally the next treatment option but, especially in polycystic ovarian syndrome, are associated with increased risk of severe ovarian hyperstimulation syndrome and high multiple pregnancies. Therefore, an effective oral treatment that could be used without risk of ovarian hyperstimulation syndrome and with minimal monitoring is preferred. It was hypothesised that aromatase inhibitors can be administered early in the follicular phase to induce ovulation by releasing the hypothalamus and/or pituitary from oestrogen negative feedback. The success of aromatase inhibitors in induction and augmentation of ovulation has been reported. In addition, increased intraovarian androgen levels may synergise with central effects of decreased oestrogen to enhance ovarian response to gonadotrophin stimulation. This increased sensitivity to follicle-stimulating hormone may be especially useful in poor responders. The potential future applications for aromatase inhibitors in infertility management are also discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".