Birth weight and premature ovarian insufficiency: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Objective In clinical work, it has been found that low birth weight will affect the ovarian function of women in adulthood, and may cause premature ovarian insufficiency, but there is no clear conclusion on this issue. We designed this meta-analysis to summarize the current evidence on the relationship between birth weight and premature ovarian insufficiency. Methods We performed a systematic review of the literature by searching MEDLINE, EMBASE, Web of Science, Scopus, Wanfang and CNKI up to August 2023. Data were combined using inverse-variance weighted meta-analysis with fixed and random effects models and between-study heterogeneity evaluated. We evaluated risk of bias using the Newcastle Ottawa Scale and using Egger’s method to test publication bias. Results Five articles were included in the review. Most of the studies were from North America and European countries, and most of the articles had a low risk of bias. A total of 2248594 women were included, including 21,813 (1%) cases of premature ovarian insufficiency, 150743 cases of low birth weight, and 220703 cases of macrosomia. We found strong evidence that low birth weight is associated with an increased risk of premature ovarian insufficiency (OR = 1.15, 95%CI 1.09–1.22) in adulthood compared with normal birth weight, and no evidence of heterogeneity was found. No effect of macrosomia on premature ovarian insufficiency was found. Conclusions Our meta-analysis showed strong evidence of an association between low birth weight and premature ovarian insufficiency. We should strengthen nutrition during pregnancy to avoid premature ovarian insufficiency caused by low birth weight.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".