Control of LH secretory-burst frequency and interpulse-interval regularity in women
Bibliographic record
Abstract
Hypothalamic neurons generate discrete bursts of gonadotropin-releasing hormone (GnRH) and thereby pulses of luteinizing hormone (LH) at randomly timed intervals centered on a probabilistic mean frequency. We tested the hypothesis that physiological mechanisms govern not only the number but also the stochastic dispersion of the GnRH/LH pulse-renewal process in humans; for example, in young women in the early (EF) and late (LF) follicular and midluteal (ML) phases of the menstrual cycle (n = 18) and in postmenopausal individuals (PM, n = 16). To this end, we quantify stochastic interpulse variability by way of the order-independent, two-parameter Weibull renewal process (Keenan DM and Veldhuis J. Am J Physiol Regul Integr Comp Physiol 281: R1917-R1924, 2001) and the sequence-specific, model-free approximate-entropy statistic (ApEn) (Pincus SM. Proc Natl Acad Sci USA 88: 2297-2301, 1991). Statistical testing unveiled 1) reduced probabilistic mean LH secretory-burst frequency (lower lambda of the Weibull distribution) in ML compared with each of EF, LF, and PM (P < 0.001); 2) quantifiably more regular LH interburst-interval sets (elevated gamma of the Weibull density) in PM than in each of EF, LF, and ML (P < 0.01); 3) uniquely prolonged latency to maximal LH secretion within individual secretory bursts in ML (P < 0.01); and 4) comparably mean random, sequential LH interburst-interval and mass values (normalized ApEn) among the distinct hormonal milieus. From these data, we postulate that sex steroids and age determine daily LH secretory-burst number, quantifiable pulse-renewal variability, and secretory-waveform evolution.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".