Effect of electromagnetic field on abortion: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background The increasing use of new technologies by pregnant women inevitably exposes them to the risks of the electromagnetic fields (EMFs). According to the World Health Organization, EMFs are the major sources of pollutants which harm human health. This study was aimed to evaluate the effects of EMF exposure on abortion. Methods Web of Science, Cochrane Library, MEDLINE, PubMed, EMBASE, Scopus, and Google Scholar were searched until 2021. Pooled odds ratio (OR) with 95% confidence interval (CI) was estimated using a random-effects model. Heterogeneity was explored using Cochran’s Q test and I 2 index. A meta-regression method was employed to investigate the factors affecting heterogeneity between the studies. The Newcastle-Ottawa scale was used to assess the credibility of the studies. Results Eligible studies (N = 17) were analyzed with a total of 57,693 participants. The mean maternal age (95% CI) was 31.06 years (27.32–34.80). Based on meta-analysis results, the pooled estimate for OR of EMF with its effects was 1.27 (95% CI: 1.10–1.46). According to the results of meta-regression, sample size had a significant effect on heterogeneity between studies (p: 0.030), but mother’s age and publication year had no significant effect on heterogeneity (p-value of bothwere >0.05). No publication bias was observed. Conclusion Exposure to EMFs above 50 Hz or 16 mG is associated with 1.27× increased risk of abortion. It may be prudent to advise women against this potentially important environmental hazard. Indeed, pregnant women should receive tailored counselling.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.008 | 0.007 |
| 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".