Distinct urinary progesterone metabolite profiles during the luteal phase
Bibliographic record
Abstract
OBJECTIVES: During normal menstrual cycles, serum levels of progesterone vary widely between cycles of same woman and between women. This study investigated the profiles of pregnanediol during the luteal phase. METHODS: Data stemmed from a previous multicenter prospective observational study and concerned 107 women (who contributed 326 menstrual cycles). The study analyzed changes in observed cervical mucus discharge, various hormones in first morning urine, and serum progesterone. Transvaginal ultrasonography and cervical mucus helped identifying the day of ovulation. Changes in pregnanediol glucuronide levels during the luteal phase were examined and classified according to the length of that phase, a location parameter, and a scale parameter. Associations between nine pregnanediol glucuronide profiles and other hormone profiles were examined. RESULTS: Low periovulatory pregnanediol glucuronide levels and low periovulatory luteinizing hormone levels were associated with delayed increases in pregnanediol glucuronide after ovulation. That 'delayed increase profile' was more frequently associated with cycles with prolonged high LH levels than in cycles with rapid pregnanediol glucuronide increases. A 'plateau-like profile' during the luteal phase was associated with longer cycles, cycles with higher estrone-3-glucuronide and pregnanediol glucuronide during the preovulatory phase, and cycles with higher periovulatory pregnanediol glucuronide levels. CONCLUSIONS: Distinct profiles of urinary progesterone levels are displayed during the luteal phase. These profiles relate to early hormone changes during the menstrual cycle. In everyday clinical practice, these findings provide further evidence for recommending progesterone test seven days after the mucus peak day. The search for other correlations and associations is underway.
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 | 0.000 | 0.001 |
| 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".