Risk Prediction Models of Natural Menopause Onset: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Predicting the onset of menopause is important for family planning and to ensure prompt intervention in women at risk of developing menopause-related diseases. OBJECTIVE: We aimed to summarize risk prediction models of natural menopause onset and their performance. METHODS: Five bibliographic databases were searched up to March 2022. We included prospective studies on perimenopausal women or women in menopausal transition that reported either a univariable or multivariable model for risk prediction of natural menopause onset. Two authors independently extracted data according to the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist. Risk of bias was assessed using a prediction model risk of bias assessment tool (PROBAST). RESULTS: Of 8132 references identified, we included 14 articles based on 8 unique studies comprising 9588 women (mainly Caucasian) and 3289 natural menopause events. All included studies used onset of natural menopause (ONM) as outcome, while 4 studies also predicted early ONM. Overall, there were 180 risk prediction models investigated, with age, anti-Müllerian hormone, and follicle-stimulating hormone being the most investigated predictors. Estimated C-statistic for the prediction models ranged from 0.62 to 0.95. Although all studies were rated at high risk of bias mainly due to the methodological concerns related to the statistical analysis, their applicability was satisfactory. CONCLUSION: Predictive performance and generalizability of current prediction models on ONM is limited given that these models were generated from studies at high risk of bias and from specific populations/ethnicities. Although in certain settings such models may be useful, efforts to improve their performance are needed as use becomes more widespread.
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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 | 0.029 | 0.144 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".