Sensory and motor stimulation thresholds of the ulnar nerve from electric and magnetic field stimuli: Implications to gradient coil operation
Bibliographic record
Abstract
Rapidly changing magnetic fields from gradient coils induce electric fields in the individual being imaged, which can potentially result in peripheral nerve stimulation (PNS). This is a safety concern in MRI. Nerves exposed to either electric fields or time-varying magnetic fields are presumed to display equivalent stimulation threshold characteristics. This assumption has motivated the use of electric stimulation literature to be applied to gradient field safety standards. The consistency of peripheral nerve stimulation thresholds were compared by measuring chronaxie times for electric and magnetic stimulation for both motor and sensory fibers in the ulnar nerve for a group of healthy volunteers. Thresholds were determined with both electromyography and also by having the subjects report stimulation onset. Chronaxie times measured between motor and sensory fibers were statistically different. However, this difference does not account for the substantial discrepancy reported between measured electric and magnetic stimulation chronaxie times. We further establish that sensation threshold as defined perceptually by the subject volunteer is adequate as a simple and reliable measurement tool. Based on these observations, significant adjustments may need to be made to nerve parameters taken from the electric field stimulation literature prior to applying them directly to gradient induced stimulation in MRI.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".