The impact of electroconvulsive therapy (ECT) on cognitive function and quality of life in individuals with schizophrenia
Bibliographic record
Abstract
Objective. This study investigates the impact of electroconvulsive therapy (ECT) on cognitive function and quality of life (QoL) in individuals with schizophrenia. Schizophrenia, often resistant to conventional treatments, may benefit from alternative approaches such as ECT. While ECT is effective in managing psychiatric symptoms, its effects on cognitive functioning and QoL remain debated. Methods. A retrospective cohort study was conducted on 66 patients with schizophrenia who underwent ECT between January and June 2024. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), psychiatric symptoms were evaluated with the Brief Psychiatric Rating Scale (BPRS), and QoL was measured using the EuroQol-5-Dimension (EQ-5D) questionnaire. These measures were taken before and after six sessions of ECT. Results. The results indicated significant improvements in psychiatric symptoms, with BPRS scores decreasing from 51.73 to 36.41 (p < 0.001). QoL also improved, with statistically significant gains in EQ-5D utility and subdomain scores (p < 0.001). Cognitive outcomes were more varied: 42.4% of patients showed cognitive improvement, while 25.8% experienced deterioration. Improvements in BPRS scores were positively correlated with better QoL, particularly in the pain and anxiety subdomains. Cognitive improvement, as indicated by MoCA scores, was linked to better QoL in the “usual activities” subdomain (p = 0.008). Conclusion. ECT shows promise in improving psychiatric symptoms and QoL in schizophrenia patients, but cognitive effects remain inconsistent. Further research is needed to optimize cognitive outcomes and assess the long-term impact of ECT in this population.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".