The global prevalence of primary ovarian insufficiency and early menopause: a meta-analysis
Bibliographic record
Abstract
Objective: The aim of this study was to estimate the global prevalence of primary ovarian insufficiency (POI) and early menopause (EM).Methods: A comprehensive literature search was performed in several databases to retrieve relevant English articles published between 1980 and 2017. To assess the methodological quality of the studies, the Newcastle-Ottawa Scale was used. The heterogeneity of results across the studies was assessed using Cochran’s Q test and quantified by the I2 statistic. Prevalence estimates of all studies were pooled using a random-effects meta-analysis model at a confidence level of 95%.Results: A total of 8937 potentially relevant articles were identified from the initial searches. Thirty-one studies met the inclusion criteria and were included in this meta-analysis. The pooled prevalence of POI and EM was calculated as 3.7% (95% confidence interval: 3.1, 4.3) and 12.2% (95% confidence interval: 10.5, 14), respectively. The prevalence of POI was higher in medium and low Human Development Index countries. The prevalence trend did not change over time.Conclusion: The prevalence of POI and EM in women is considerable. The results of this study could contribute to consciousness-raising of health policy-makers toward the necessity of prioritizing, planning, and allocating health resources as preventive and treatment interventions for these women.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".