Does Molimina Indicate Ovulation? Prospective Data in a Hormonally Documented Single-Cycle in Spontaneously Menstruating Women
Bibliographic record
Abstract
Approximately 33% of normal-length (21–35 days) cycles have subclinical ovulatory disturbances and lack sufficient progesterone, although their normal length ensures enough estrogen. Subclinical ovulatory disturbances are related to significant premenopausal spine bone loss (−0.86%/year). Molimina, non-distressing premenstrual experiences, may detect ovulation within normal-length cycles. This prospective study assessed the relationship between molimina and ovulation. After 1-cycle of daily diary and first morning urine collections, women answered the Molimina Question (MQ): “Can you tell by the way you feel that your period is coming?” and were invited to share (a) predictive premenstrual experience(s). A 3-fold increase in follicular-luteal pregnanediol levels confirmed ovulation. In 610 spontaneously menstruating women (not on hormonal contraception, mean age 31.5 ± 5.3, menarche age 12.7 ± 1.5, cycle length [CL] 29 days, MQ positive in 89%), reported premenstrual experiences which included negative moods (62%), cramps (48%), bloating (39%), and front (26%) or axillary (25%) breast tenderness. Of 432 women with pregnanediol-documented cycles, 398 (92%) were ovulatory (CL: 29 ± 5) and 34 (8%) had ovulatory disturbances (CL: 32 ± 14). Women with/without ovulatory cycles were similar in parity, body mass index, smoking, dietary restraint and the MQ; ovulatory-disturbed cycles were longer. Molimina did not confirm ovulation. A non-invasive, inexpensive ovulation indicator is needed to prevent osteoporosis.
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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 | 0.005 |
| 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 | 0.001 | 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".