PILOT STUDY OF THE CLINICAL AND COGNITIVE EFFECTS OF HIGH-FREQUENCY MAGNETIC SEIZURE THERAPY IN MAJOR DEPRESSIVE DISORDER
Bibliographic record
Abstract
BACKGROUND: Electroconvulsive therapy (ECT) is a very commonly used treatment for patients with severe and treatment-resistant depression. Although effective, this treatment is complicated by a number of side effects including cognitive impairment motivating attempts to develop treatment alternatives. Magnetic seizure therapy (MST) is a brain stimulation technique using a high-powered transcranial magnetic stimulation device to produce therapeutic seizures. Preliminary research suggests that MST has antidepressant activity in the absence of cognitive side effects. The aim of this study was therefore to investigate the therapeutic efficacy and cognitive profile of MST provided at high frequency (100 Hz) and potentially longer stimulation trains and longer treatment courses than have been previously investigated. METHODS: Thirteen patients participated in an open-label clinical trial of up to 18 treatment sessions with 100-Hz MST. Assessments of depression severity and cognitive functioning were performed before and after treatment. RESULTS: Of the 13 patients who completed the study, five met clinical response criteria at study end. There was an overall group reduction in depression severity and no evidence of any impairment of orientation, memory, or other elements of cognition after MST treatment. The major limitation of the study was its lack of sham control. CONCLUSIONS: In conclusion, MST shows antidepressant efficacy without apparent cognitive side effects. However, substantial research is required to understand the optimal conditions for stimulation and to compare MST to established treatments including ECT.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".