Modeling Fertile Window Differences Across the Reproductive Lifespan with Quantitative Urine Hormone Monitoring of the Menstrual Cycle
Bibliographic record
Abstract
BACKGROUND: Reproductive hormones of the fertile window are often referenced to women in regular cycles, but this may not be representative of the hormonal profiles of women in different circumstances like polycystic ovarian syndrome, the postpartum period, and the perimenopause transition. This observational cohort study sought to identify the variability in the reproductive hormones in various clinical circumstances and to establish potential thresholds for each category based on hormone measurements with the Mira urinary hormone monitor. RESULTS: A total of 57 women (ages 22-51) in various circumstances (regular cycles, polycystic ovarian syndrome, postpartum and perimenopause) tracked Mira urine hormone measurements (estrone-3-glucuronide, luteinizing hormone, pregnanediol glucuronide), contributing 444 cycles of data. Using additive mixed models, hormone values were stratified by the four different reproductive categories. The perimenopause and polycystic ovarian syndrome groups demonstrated relative hypoestrogenic states, while the perimenopause group showed low luteal pregnanediol glucuronide and the polycystic ovarian syndrome/polyendocrine metabolic ovarian syndrome (PCOS/PMOS) group showed high luteal pregnanediol glucuronide. The perimenopause group had significantly higher luteinizing hormone values throughout the whole cycle. CONCLUSION: The fertile window hormone thresholds vary depending on a woman's specific reproductive category. Women in different circumstances should not necessarily use the same hormonal thresholds for the fertile window and ovulation. A larger dataset with ultrasound correlation to ovulation is required to delineate the fertile window with more precision. Hormone differences across the menstrual cycle could be used for targeted treatments in polycystic ovarian syndrome and perimenopause women.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".