Human antral folliculogenesis: what we have learned from the bovine and equine models.
Bibliographic record
Abstract
The study of ovarian folliculogenesis has been of great interest to scientists and clinicians in the human and veterinary health fields for more than 20 centuries. Initial studies of the ovarian follicle were based on anatomical descriptions post-mortem, followed by histologic and endocrinologic evaluation of ovarian status. The introduction of high resolution ultrasonography in the 1980s provided a long-awaited tool to image the reproductive tissues in situ in both animal and human species. The bovine and equine species have been established as models for the study of human ovarian folliculogenesis. Profound similarities in the dynamics of follicle development exist between the menstrual cycle in humans and the estrous cycle in cattle and horses. Disparities between species appear specific rather than general. Research performed in women thus far has led to the concepts that: 1) follicle development occurs in a wave-like manner during the menstrual cycle, 2) the number of waves per cycle correlates positively with the length of the cycle, 3) the emergence of follicle waves in women are preceded by a rise in circulating FSH, 4) selection of a dominant follicle may occur in each wave of the cycle, and 5) a decline in circulating FSH and increase in follicular estradiol, inhibin A, and IGF-II act collectively to enable the dominant follicle to continue to grow in an endocrine environment of decreasing FSH and increasing LH, while subordinate follicles undergo regression. The goal of continued research using animal models for studying human ovarian function is to provide the hypothetical basis for further studies in women, which will ultimately lead to the development of safer and more efficacious infertility and contraceptive therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".