A Retrospective Study of Cognitive Improvement Following Electroconvulsive Therapy in Schizophrenia Inpatients
Bibliographic record
Abstract
OBJECTIVES: Findings on the cognitive effect of electroconvulsive therapy (ECT) in individuals with schizophrenia have brought mixed results, with few recent studies beginning to report cognitive improvements after treatment. Cognitive change in inpatients with schizophrenia who were referred for an acute course of ECT was examined in the current study. Furthermore, the study aimed to determine the profile of patients who experience cognitive improvement and the potential use of a brief cognitive battery to detect this positive cognitive change, if any. METHODS: Montreal Cognitive Assessment (MoCA) was conducted at baseline and posttreatment after 6 sessions of ECT. The Brief ECT Cognitive Screen was also administered to determine its predictive ability on cognitive gain of 2 points or higher in MoCA total scores for the 2 consecutive time points. RESULTS: A total of 81 inpatients were included in the study. Retrospective analysis revealed significant improvements in MoCA total score and domains of visuospatial/executive function and attention. Cognitive improvement was more pronounced among those who had worse pre-MoCA score before ECT. CONCLUSIONS: The study provided support to the existing literature where cognitive improvement has been reported among individuals with schizophrenia after ECT. Future studies should consider the use of randomized controlled trials to examine the possible cognitive benefits of ECT. In a setting where there is a high volume of patients receiving ECT, the monitoring of patients' cognitive status through the course of ECT continues to be warranted and the Brief ECT Cognitive Screen may be useful as a quick measure to detect such ECT-related cognitive change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".