Bibliographic record
Abstract
Ovulatory disorders (such as polycystic ovary syndrome [PCOS]) are commonly treated with clomiphene citrate (CC), with rates of ovulation ranging between 50 and 90%. However, even though the majority of pregnancies occur within the first three cycles of treatment [1], the overall pregnancy rates are disappointingly low, ranging from 20–40% [2–4], and the likelihood of miscarriage is relatively high [5]. This discrepancy between high rates of ovulation and low rates of pregnancy is believed to be a consequence of the antiestrogenic effect of CC on peripheral targets, such as the endometrium (affecting its thickness and maturation) and the endocervix (affecting the production and quality of cervical mucus). The problem is compounded by the long half-life of CC and the persistence of an isomer, zuclomiphene, both of which lead to an accumulating antiestrogenic effect with consecutive cycles of treatment [6]. In addition, due to the prolonged occupation of estrogen receptors by CC in the hypothalamus, the circulating amount of estrogen may rise to high levels from ongoing stimulation of the follicles by endogenous follicle-stimulating hormone (FSH) production. Such supraphysiological levels of estrogen may be deleterious to the developing oocyte, sperm and embryo [7]. Consequently, better methods for inducing ovulation are desirable. Studies in animals suggest that aromatase, an enzyme necessary to convert androgens to estrogen, could be a potential target for the inhibition of estrogen production. Aromatase inhibitors (AIs) increased gonadotropin levels and ovarian weight in female rats [8], and have been found to produce multiple ovarian follicles in female primates [9]. The new generation of AIs (anastrozole, letrozole and vorozole) have more specific action, fewer side effects and lower
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".