Fluctuations in resting motor threshold during electroconvulsive and magnetic seizure therapy
Bibliographic record
Abstract
OBJECTIVES: Magnetic seizure therapy (MST) is more benign than electroconvulsive therapy (ECT) in terms of cognitive impairment. However, whether these two 'artificial seizures' facilitate the central motor neural pathway and the motor cortical effects have not been investigated. The study aimed to compare the effects of ECT and MST on motor-evoked potential (MEP) in patients with mental disorders. METHODS: 6 bifrontal ECT treatments in a nonrandomized comparative clinical design. Data on the duration of motor seizures were collected for each treatment. MEP latency and the resting motor threshold (rMT) were measured at baseline and after every two treatments. Comparisons were performed between or within the groups. RESULTS: Seizure durations were significantly longer in the ECT group compared to the MST group across multiple sessions. Both MST and ECT demonstrated a significant reduction in rMT in the left and right hemispheres after the fourth (T3) and sixth treatments (T4) compared to baseline (T1). However, there were no significant changes in MEP latency within or between the groups throughout the treatment sessions. The only difference was that the rMT in the left cerebral hemisphere was significantly lower after T4 than after the second treatment (T2). There was no difference in rMT between the ECT and MST groups. CONCLUSIONS: Both ECT and MST facilitate the central motor pathway, with a shared mechanism of increased motor cortex excitability.
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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 | 0.002 |
| 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".