Greater prevalence of symptoms associated with higher exposures to mobile phone base stations in a hilly, densely populated city in Mizoram, India
Bibliographic record
Abstract
Members of the scientific community and the general public are raising concerns about the potential health and environmental effects of radio-frequency electromagnetic fields (RF-EMF) for those living nearby mobile phone base stations (MPBS). This study examined the impact of RF-EMF (900–1900 MHz) on symptoms spanning four health categories: mood-energy, cognitive-sensory, inflammatory, and anatomical issues. A questionnaire identifying health symptoms within these categories, was given to 183 highly exposed and 126 reference residents, matched on demographics. While years of residing near the MPBS influenced the prevalence of some symptoms, proximity to the base station and higher levels of exposure (measured using power density) influenced the prevalence of many of the symptoms. A higher proportion of symptoms was found in residents who were either living within 50 meters of a MPBS or who were exposed to power densities of 5–8 mW/m2, for all four health categories. This relationship between exposure level and symptom prevalence was further influenced by age, daily mobile phone use (over 5 h per day), and lifestyle factors, for certain symptoms. Hierarchical regression analysis revealed that level of exposure (power density) was the only factor contributing to the number of symptoms experienced by residents, for all four health categories. An unexpected finding was that among the more highly exposed residents, the younger individuals (under 40 years) reported more inflammation related issues than older individuals. These results underscore the need to inform policymakers regarding the benefits of adopting a precautionary approach to potential risks associated with RF-EMF exposures from MPBS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".