Magnetic Seizure Therapy in Treatment-Resistant Schizophrenia: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: Electroconvulsive therapy is effective in treatment-resistant schizophrenia (TRS) but use is limited due to stigma and concerns around cognitive adverse effects. Magnetic seizure therapy (MST) is a promising new neuromodulation technique that uses transcranial magnetic stimulation to induce therapeutic seizures. Studies of MST in depression have shown clinical improvement with a favorable adverse effect profile. No studies have examined the clinical utility of MST in schizophrenia. METHODS: We conducted an open-label pilot clinical trial of MST in eight TRS patients. Up to 24 MST treatments were delivered depending on treatment response. We assessed clinical outcome through the Brief Psychiatric Rating Scale (BPRS) and the Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q). Cognitive testing included a neuropsychological test battery, the Autobiographical Memory Inventory (AMI), Montreal Cognitive Assessment (MoCA), and reorientation time. RESULTS: Four patients completed the trial as per protocol. For all patients and for trial completers alone, there was a significant clinical and quality of life improvement. Three met pre-determined criteria for remission (total score ≤25 on the BPRS) and one met criteria for response (i.e., ≥25% BPRS improvement from baseline for two consecutive assessments). Pre and post neurocognitive data showed no significant cognitive adverse effects apart from a decrease in AMI scores. CONCLUSION: In this pilot study, MST demonstrated evidence for feasibility in patients with TRS, with promise for clinical efficacy and negligible cognitive side effects. Further study in larger clinical populations is needed. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, Identifier NCT01596608.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".