Validating At-Home Urinary Hormone Measurements in Postpartum and Perimenopause Fertility Transitions
Bibliographic record
Abstract
Background: Measuring quantitative menstrual cycle hormones at home may help women better understand their postpartum and perimenopause fertility transitions, but these quantitative fertility monitors require validation. Materials and Methods: This study included 16 North American women, aged 28–51, during either the postpartum ( n = 8, cycles = 18) or perimenopause ( n = 8, cycles = 35) fertility transitions testing daily first-morning urine testing with both the Mira Monitor and ClearBlue Fertility Monitor (CBFM) along with menstrual cycle parameter tracking. The main outcome measures were a rise in estrone-3-glucuronide (E 1 3G) and luteinizing hormone (LH) urine hormone values from the Mira monitor correlated to low, high, or peak values on the CBFM. Results: Both in the postpartum and perimenopause transitions, the identification of the day of ovulation based on the LH surge on the Mira and CBFM monitors was highly correlated (R = 0.94 and 0.83, p < 0.001). The E 1 3G levels on the Mira monitor were significantly higher for a CBFM reading of “High” compared with “Low” for both the postpartum and perimenopausal cycles (all p < 0.001). Similarly, the LH levels on the Mira monitor were significantly higher for a CBFM reading of “Peak” (LH surge) compared with “High” for both the postpartum and perimenopausal cycles (all p < 0.001). Conclusions: The LH surge and levels of E 1 3G in urine identified on the quantitative Mira fertility monitor strongly correlate to the LH surge and the shift from low to high on the CBFM during the postpartum and perimenopause transitions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".