Individualized predictions of time to menopause using multiple measurements of antimüllerian hormone
Bibliographic record
Abstract
OBJECTIVE: The ability of antimüllerian hormone (AMH) to predict age at menopause has been reported in several studies, and a decrease in AMH level has been found to increase the probability of menopause. The rate of decline varies among women, and there is also a variability of decline between women's cycles. As a result, individualized evaluation is required to accurately predict time of menopause. To this end, we have used the AMH trajectories of individual women to predict each one's age at menopause. METHODS: From a cohort study, 266 women (ages 20-50 y) who had regular and predictable menstrual cycles at the initiation of the study were randomly selected from among 1,265 women for multiple AMH measurements. Participants were visited at approximately 3-year intervals and followed for an average of 6.5 years. Individual likelihood of menopause was predicted by fitting the shared random-effects joint model to the baseline covariates and the specific AMH trajectory of each woman. RESULTS: In total, 23.7% of the women reached menopause during the follow-up period. The estimated mean (SD) AMH concentration at the time of menopause was 0.05 ng/mL (0.06 ng/mL), compared with 1.36 ng/mL (1.85 ng/mL) for those with a regular menstrual cycle at their last assessment. The decline rate in the AMH level varied among age groups, and age was a significant prognostic factor for AMH level (P < 0.001). Adjusting for age and body mass index, each woman had her own specific AMH trajectory. Lower AMH and older age had significant effects on the onset of menopause. Individualized prediction of time to menopause was obtained from the fitted model. CONCLUSIONS: Longitudinal measurements of AMH will enable physicians to individualize the prediction of menopause, thereby facilitating counseling on the timing of childbearing or medical management of health issues associated with menopause.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".