Peripheral Nerve Stimulation Thresholds Based on Waveform Shape and Implications for Guideline Limits
Bibliographic record
Abstract
ABSTRACT: The objective of this paper is to derive basic restrictions for induced internal electric field and reference levels for external magnetic flux density for a class of periodic non-sinusoidal waveforms as multiples of the existing limits applicable to sinusoidal waveforms in current exposure standards. The Law of Electrostimulation and the Spatially Extended Nonlinear Node computational model were used to derive peripheral nerve stimulation thresholds of the internal electric field for both non-sinusoidal and sinusoidal waveforms. Threshold ratios (non-sinusoidal to sinusoidal) permitted basic restrictions and reference levels to be derived as multiples of the sinusoidal ones. Intercomparisons of threshold ratios from both models suggest that they are in agreement for flat-topped flux density waveforms with fast rise-times relative to the period but showed a discrepancy for the continuous sinusoid. Results from the computational model were used to establish the threshold ratios used in the conversion. Resulting non-sinusoidal basic restrictions and reference levels were found to have the same functional relationship with frequency as the sinusoidal ones, consisting of two ranges: a flat rheobase and a frequency-dependent (basic restriction) or inverse frequency-dependent (reference level) portion that intersects the rheobase at a transition frequency that is waveform-dependent. Above the transition frequency, the non-sinusoidal basic restriction was found to be inversely related to the flux density rise-time, resulting in an increased limit for fast-rising waveforms. The transition frequencies of fast-rising waveforms were found to be lowered relative to the sinusoidal one. Above the same transition frequency, the non-sinusoidal reference level is flat with frequency and was found to be approximately 79% lower than the sinusoidal one.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".