Faculty Opinions recommendation of Anti-Müllerian hormone and inhibin B variability during normal menstrual cycles.
Bibliographic record
Abstract
Objective-Describe anti-Müllerian hormone (AMH) variation across normal menstrual cycles. Design-Cohort study Setting-Academic environment Patients-Twenty regularly-menstruating women Interventions-Serum AMH and inhibin B assayed daily during one normal menstrual cycle Main Outcome Measures-Intracycle variability of AMH and inhibin BResults-Data was classified into quartiles of AMH area-under-the-curve (AUCs).Mean AMH AUC was 15.7 ng/ml for Quartile 1 vs. 43.5, 80.9 and 144.9 ng/ml for Quartiles 2, 3 and 4. Mean AMH levels (ng/ml) were 0.67, 1.71, 3.02, and 5.33, respectively.There was no variation in Quartile 1 AMH rate of change from stochastic modeling, but in Quartiles 2-4, there were increased rates of change in days 2-7.Women in Quartile 1 had the lowest mean inhibin B (24.2 pg/ml vs. 44.3,43.2, and 42.2 pg/ml) and had shorter menstrual cycles (24.6 days) than women in Quartiles 3 and 4 (28.2 and 28.4 days).Conclusions-There were two menstrual cycle patterns of AMH.The "aging ovary" pattern included low AMH levels with little variation, lower inhibin B and shorter cycle lengths.The "younger ovary" pattern included higher AMH levels with significant variation days 2-7, suggesting that for women with AMH >1 ng/ml, the interpretation of AMH levels is contingent upon the day of the menstrual cycle on which specimen is obtained.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".