Birth weight and premature ovarian insufficiency: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To comprehensively evaluate the effect of low birth weight on 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. All cohort and case-control studies that included birth weight as an exposure and premature ovarian insufficiency as an outcome were included in the analysis. 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. All statistical analyses were performed with the use of R software. RESULTS: Five articles were included in the review. A total of 2,248,594 women were included, including 21,813 (1%) cases of premature ovarian insufficiency, 150,743 cases of low birth weight, and 220,703 cases of macrosomia. We found strong evidence that changed the results of the previous review 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. 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 reduce the occurrence of low birth weight by various methods to avoid the occurrence of premature ovarian insufficiency.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".