The Influence of Positioning and Muscle Activity on Motor Threshold during Motor Cortex Stimulation Programming
Bibliographic record
Abstract
Background: Stimulation parameters are crucial for the efficacy and safety of motor cortex stimulation (MCS). Motor threshold (MT) can be defined as the lowest voltage that produces motor contraction. The final stimulation parameters are always a smaller percentage of MT in order to avoid seizures. We determined how patient position and activity affect MT. Methods: Prospective MT measurements were made while patients were either lying down or sitting up, and in a resting state or while actively contracting the target muscle. Paired 1-tailed t tests were performed to assess for statistically significant differences between MT measurements made under the 4 different combinations of position and activity. Results: The MT was lower when the target muscle was being actively contracted compared to resting in both supine and upright positions (both p < 0.001). The MT was also lower when upright compared to supine in both resting and active states of muscle contraction (both p < 0.001). The mean difference between supine resting and upright active states is 0.79 V. Conclusion: When selecting final stimulation parameters for MCS, clinicians should be aware that the lowest MT is elicited while patients are seated upright and actively contracting the target muscle. Using this method of determining the MT when calculating the final stimulation parameters could reduce the chance of MCS-induced seizures. © 2015 S. Karger AG, Basel.
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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 | 0.004 |
| 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 | 0.000 | 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".