A Pilot Study of Switching Electroconvulsive Therapy for Patients With Treatment Resistant Schizophrenia or Mood Disorder
Bibliographic record
Abstract
OBJECTIVES: Switching of ECT electrode modality is commonly done in clinical practice but outcomes are unclear. We aimed to compare the clinical outcomes between ECT modality switchers and nonswitchers in a large tertiary psychiatric institution over 1 year. METHODS: Brief Psychiatric Rating Scale (BPRS), Montgomery-Åsberg Depression Rating Scale (MADRS) and Montreal Cognitive Assessment (MoCA) were used to assess symptoms and cognition. General linear regression was utilized to compare the change of BPRS or MADRS and MoCA score among switchers vs nonswitchers. RESULTS: 21.5% of 209 patients switched ECT. Baseline BPRS scores were lower among nonswitchers. Response rate in schizophrenia, depression and mania were higher for nonswitchers (69.6%, 81.35% and 84.8% respectively / 9.2 (SD 3.3) sessions) compared to switchers (53.8%, 0% and 66.7% respectively / 10.6 (SD 4.5) sessions). Most common ECT switches were Bifrontal (BF) to Bitemporal (BT) (schizophrenia), UB RUL (ultrabrief right unilateral) to BT (depression), and UB RUL to BT / BF (mania). There was no significant difference in the change of BPRS and MoCA scores between nonswitchers and switchers. However, there was significantly more improvement of MADRS scores among nonswitchers [adjusted mean ± SE: (-26.4 ± 2.8)] compared with switchers (-10.6) ±6.6). CONCLUSIONS: ECT switching was commonly done and may result in better or worse outcomes than not switching depending on diagnosis. Controlled trials are required to address this urgent clinical issue.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".