Interrelationships among reproductive hormones and antral follicle count in human menstrual cycles
Bibliographic record
Abstract
It is recognised that ovarian factors, including steroid and protein hormones, are critical in the feedback regulation of pituitary gonadotropins; however, their individual contributions are less defined. The aim of this study was to explore the reciprocal relationships between ovarian and pituitary hormones across the normal ovulatory menstrual cycle as women age. FSH, LH, oestradiol, progesterone, inhibin A, inhibin B and anti-mullerian hormone (AMH) were measured in serum collected every 1-3 days across one interovulatory interval (IOI) from 26 healthy women aged 18-50 years. The antral follicle count (AFC) for follicles 2-5 mm, >6 mm and 2-10 mm were tabulated across the IOI. Independent associations between ovarian hormones/AFC vs pituitary follicle-stimulating hormone (FSH) and luteinising hormone (LH) were investigated using multivariate regression analysis. The data were sub-grouped based on the presence or absence luteal phase-dominant follicles (LPDF). Serum oestradiol and AMH were inversely correlated with FSH in both follicular and luteal phases. Inhibin B correlated inversely with FSH and LH in the late follicular phase and directly in the luteal phase. AFC, inhibin A and progesterone were not key predictors of either FSH or LH. The strong association between AMH and FSH with age implies that AMH, as well as oestradiol and inhibin B are important regulators of FSH. The change in feedback response of inhibin B with both FSH and LH across the cycle suggests two phases of the negative feedback.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".