Meta-analysis suggests that smoking is associated with an increased risk of early natural menopause
Bibliographic record
Abstract
OBJECTIVE: Age at natural menopause (ANM) is usually defined as the age at the last menstrual bleeding followed by the absence of menses for 12 consecutive months. Although many studies have suggested an association between smoking and early age at natural menopause, evidence remains conflicting because some studies reported inconsistent or contrasting results. To resolve this ambiguity and to quantitatively evaluate the effect of smoking on ANM, we conducted a meta-analysis of the available data about smoking and ANM. METHODS: After extensive searching of public literature databases, a total of 11 studies were selected for this meta-analysis. Among them, the phenotype of the participants in five studies (dichotomous studies) was classified as early or late ANM, and odds ratio (OR) was used to evaluate the effect of smoking on early ANM. For the other six studies (continuous studies), mean and SD were provided for smoking and nonsmoking samples, and weighted mean difference (WMD) was used as the effect size. RESULTS: We found that smoking was significantly associated with early ANM in both dichotomous and continuous studies. The pooled effect was OR = 0.74 (95% CI, 0.60-0.91, P < 0.01) in the dichotomous studies. For the continuous studies, the pooled effect estimated by WMD was -1.12 (95% CI, -1.80 to -0.44, P = 0.04). After adjustment of the original data for heterogeneity, the pooled results changed only a little: OR = 0.67 (95% CI, 0.61-0.73, P < 0.01) for dichotomous studies and WMD = -0.90 (95% CI, -1.58 to -0.21, P = 0.01) for the continuous studies. CONCLUSIONS: The results of our study suggest that smoking is a significant independent factor for early ANM.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.042 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".