Estimation of RF and ELF dose by anatomical location in the brain from wireless phones in the MOBI-Kids study
Bibliographic record
Abstract
Wireless phones (both mobile and cordless) emit not only radiofrequency (RF) electromagnetic fields (EMF) but also extremely low frequency (ELF) magnetic fields, both of which should be considered in epidemiological studies of the possible adverse health effects of use of such devices. This paper describes a unique algorithm, developed for the multinational case-control MOBI-Kids study, that estimates the cumulative specific energy (CSE) and the cumulative induced current density (CICD) in the brain from RF and ELF fields, respectively, for each subject in the study (aged 10-24 years old). Factors such as age, tumour location, self-reported phone models and usage patterns (laterality, call frequency/duration and hands-free use) were considered, as was the prevalence of different communication systems over time. Median CSE and CICD were substantially higher in GSM than 3G systems and varied considerably with location in the brain. Agreement between RF CSE and mobile phone use variables was moderate to null, depending on the communication system. Agreement between mobile phone use variables and ELF CICD was higher overall but also strongly dependent on communication system. Despite ELF dose distribution across the brain being more diffuse than that of RF, high correlation was observed between RF and ELF dose. The algorithm was used to systematically estimate the localised RF and ELF doses in the brain from wireless phones, which were found to be strongly dependent on location and communication system. Analysis of cartographies showed high correlation across phone models and across ages, however diagonal agreement between these cartographies suggest these factors do affect dose distribution to some level. Overall, duration and number of calls may not be adequate proxies of dose, particularly as communication systems available for voice calls tend to become more complex with time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".