Simple linear formulation for magnetostimulation specific to MRI gradient coils
Bibliographic record
Abstract
A simple linear formulation for magnetostimulation thresholds specific to MRI gradient coils is derived based on established hyperbolic electrostimulation strength vs. duration relations. Thresholds are derived in terms of the gradient excursion required to cause stimulation, and it is demonstrated that the threshold curve is a linear function of the gradient switching time. A parameter beta is introduced as being fundamental in the evaluation of gradient coil stimulation. beta is a map of the induced electric field per unit gradient slew rate, and can be calculated directly from the gradient coil wire pattern. Consideration of beta alone is sufficient to compare stimulation thresholds between different gradient coil designs, as well as to evaluate the expected dependency of stimulation threshold on position within the gradient coil. The linear gradient threshold curve is characterized by two parameters: SR(min) and DeltaG(min). SR(min) is the slope of the threshold curve and represents the minimum slew rate required to cause stimulation in the limit of infinite gradient strength. DeltaG(min) is the vertical axis intercept of the curve and represents the minimum gradient excursion required to cause stimulation in the limit of infinite slew rate. Both SR(min) and DeltaG(min) are functions of both beta and the standard tissue parameters E(r) (rheobase) and tau(c) (chronaxie time). The ease with which both the gradient system performance and the stimulation thresholds can be plotted on the same axes is noted and is used to introduce the concept of a piece-wise linear operational limit curve for a gradient system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".