Low-dose ionizing radiation and adverse birth outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Ionizing radiation is a human carcinogen, and there is evidence that exposure to low-dose ionizing radiation increases the risk of adverse birth outcomes. METHODS: We undertook a systematic review and meta-analysis to synthesize the research of maternal and paternal exposure to low-dose radiation on low birth weight, miscarriage, pre-term delivery, and stillbirth. Our literature search used four databases (PubMed, Environmental Index, GeoBASE, and the Cumulative Index to Nursing and Allied Health Literature). We included study populations exposed to occupational and medical sources of radiation, nuclear disasters, and those living near nuclear power plants. We considered papers published between January 1st, 1990, and June 30th, 2021. The quality of the studies was assessed, and we performed meta-analysis using random effects models to generate summary measures of association. Forest plots were created to assess the heterogeneity in these measures, and funnel plots were used to assess publication bias. RESULTS: Overall, 26 studies were identified, and these yielded measures of association from 10, 11, and 8 studies for low birth weight, miscarriage, and stillbirth outcomes, respectively. It was not possible to perform meta-analyses for pre-term delivery due to a small number of studies. The meta-analysis summary relative risk (RR) of having a low-birth-weight infant among those ever exposed to radiation relative to those unexposed, after adjusting for publication bias, was 1.29 (95% CI 0.97-1.73). The corresponding risk estimates for miscarriage and stillbirth were 1.15 (95% CI 1.02-1.30), and 1.19 (95% CI 0.98-1.45), respectively. CONCLUSIONS: Our findings suggest that ionizing radiation increases the risk of adverse birth outcomes. Future work should strive to provide data needed to better understand the shape of the exposure-response curve.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".