In situ electric field dosimetry analysis for powerline frequency peripheral nerve magnetic stimulation
Bibliographic record
Abstract
Humans are exposed to environmental 60 Hz magnetic fields (MFs), inducing in our body electric fields (EFs) and currents, potentially stimulating the peripheral nervous system (PNS). Uncertainties exist regarding the 60 Hz MF PNS stimulation threshold. The spatially extended nonlinear node model (SENN) is used to help define international MF exposure guidelines and standards protecting workers and the general public. However, other models exist, particularly the McIntyre-Richardson-Grill (MRG) model, the new gold standard for electrostimulation. This study aims (1) to model a new extremely low frequency MF exposure system for the human leg and (2) to investigate the in situ EFs generated by the system at 60 Hz at the skin level and in the nerves of the leg using a realistic human body model with both the SENN and the MRG models. A Helmholtz like-coil system was designed to generate in situ EFs sufficient for nerve stimulation, modeled using Biot-Savart and Faraday laws. Sim4Life simulations assessed the induced EFs at skin and nerve levels using a detailed human body model and two nerve excitation frameworks: the SENN and MRG models. High EF intensities were observed in four sensory and sensory-motor nerves, with MRG-derived thresholds lower than SENN-derived thresholds. Results also highlight the significance of nerve orientation in EF induction. This study emphasizes the critical role of comprehensive modeling for the design and validation of MF exposure systems and underscores the need for experimental data to refine models, standards, and guidelines.
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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 | 0.000 | 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) | 0.001 | 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".