Exposure to Non-Ionizing Radiation and Childhood Cancer: A Meta-Analysis
Bibliographic record
Abstract
Background: A slight increase in the childhood cancer trend has been observed for the past few decades. Non-ionizing radiation is one of the environmental factors linked to childhood cancers. This review is conducted to assess the association between non-ionizing radiation and childhood cancer based on all original studies to date. Methods: A systematic search was conducted on the titles and abstracts pertaining to non-ionizing radiation and childhood cancers using the PubMed, Scopus, SAGE and ScienceDirect databases from inception up to November 2018. Quality of each article was appraised using the Newcastle-Ottawa Scale, meta-analysis was performed with Review Manager, and fixed effects were used to estimate the pooled OR of the selected studies. Results: A total of 15 articles met all the selection criteria. Twelve articles were included in the meta-analysis. Pooled risk estimates of the 12 studies, obtained via fixed effects model, showed that children exposed to 0.2 µT or more of EMF non-ionizing radiation run 1.33 times higher risks of contracting childhood cancer compared to those with less than 0.2 µT exposure (95% CI: 1.10, 1.60). The studies were statistically homogeneous (chi-squared P=0.71, I2=0%), and there was no evidence of publication bias. Conclusion: It cannot be concluded that children exposed to non-ionizing radiation have higher risks of childhood cancer compared to those who were not exposed as claimed by the previous reviews. However, concerns about non-ionizing radiation exposure and childhood cancer should not be neglected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.070 |
| Bibliometrics | 0.006 | 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 | 0.003 |
| 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".