Hormonal Predictors of Abnormal Luteal Phases in Normally Cycling Women
Bibliographic record
Abstract
Objective: Explore potential relationships between preovulatory, periovulatory, and luteal-phase characteristics in normally cycling women. Design: Observational study. Setting: Eight European natural family planning clinics. Patient(s): Ninety-nine women contributing 266 menstrual cycles. Intervention(s): The participants collected first morning urine samples that were analyzed for estrone-3 glucuronide (E1G), pregnanediol-3- alpha-glucuronide (PDG), follicle stimulating hormone (FSH), and luteinizing hormone (LH). The participants underwent serial ovarian ultrasound examinations. Main Outcome Measure(s): Four criteria were analyzed: short luteal phase, low mid-luteal phase PDG level (mPDG), normal then low luteal PDG level, low then normal luteal PDG level. Results: A long preovulatory phase was a predictor of short luteal phase, with or without adjustment for other variables. A high periovulatory PDG level was a predictor for short luteal phase and for normal then low luteal PDG level. A low periovulatory PDG level predicted low mPDG and low then normal luteal PDG level, with or without adjustment for other variables. A small maximum follicle predicted normal then low luteal PDG level, with or without adjustment for other variables. The relationship between small maximum follicle size and short luteal phase or small maximum follicle size and low mPDG was no longer present when the regression was adjusted for certain characteristics. A younger age at menarche and a high body mass index were both predictors of low mPDG. Conclusion: Luteal phase abnormalities exist over a spectrum where some ovulation disorders may exist as deviations from the normal ovulatory process. This study confirms the negative impact of a small follicle size on the quality of the luteal phase. The occurrence of normal then low luteal PDG level is confirmed as a potential sign of luteal phase abnormality.
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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 | 0.000 | 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".