Is There Evidence That Stimulus Parameters and Electrode Placement Affect the Cognitive Side Effects of Electroconvulsive Therapy in Patients With Schizophrenia and Schizoaffective Disorder?
Bibliographic record
Abstract
ABSTRACT: Seventy percent of patients with treatment-resistant schizophrenia do not respond to clozapine. Electroconvulsive therapy (ECT) can potentially offer significant benefit in clozapine-resistant patients. However, cognitive side effects can occur with ECT and are a function of stimulus parameters and electrode placements. Thus, the objective of this article is to systematically review published clinical trials related to the effect of ECT stimulus parameters and electrode placements on cognitive side effects. We performed a systematic review of the literature up to July of 2020 for clinical studies published in English or German examining the effect of ECT stimulus parameters and/or electrode placement on cognitive side effects in patients with schizophrenia or schizoaffective disorder. The literature search generated 3 randomized, double-blind, clinical trials, 1 randomized, nonblinded trial, and 1 retrospective study. There are mixed findings regarding whether pulse width and stimulus dose impact on cognitive side effects. One study showed less cognitive side effect for right unilateral (RUL) than bitemporal (BT) electrode placement, and 2 studies showed a cognitive advantage for bifrontal (BF) compared with BT ECT. Only 1 retrospective study measured global cognition and showed post-ECT cognitive improvement with all treatment modalities using Montreal Cognitive Assessment in comparison to pre-ECT Montreal Cognitive Assessment scores. Current data are limited, but evolving. The evidence suggests that RUL or BF ECT have more favorable cognitive outcomes than BT ECT. Definitive larger clinical trials are needed to optimize parameter and electrode placement selection to minimize adverse cognitive effects.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".