Cognitive function changes during a course of electroconvulsive therapy in patients with various psychiatric illnesses: A prospective observational study
Bibliographic record
Abstract
Background: Electroconvulsive therapy (ECT) is an effective treatment for severe psychiatric disorders, but concerns about cognitive side effects persist. Aim: To assess cognitive function changes during ECT in patients with various psychiatric illnesses. Materials and Methods: This prospective observational study included 100 patients (aged 18–60 years) with psychiatric disorders scheduled for ECT at a tertiary care hospital. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) at four-time points: before and after the first ECT session, and before and after the third ECT session. The first and third sessions were chosen to capture both acute effects and short-term cumulative changes. Socio-demographic and clinical data were collected using a semi-structured proforma. Results: The sample comprised patients with schizophrenia (50%), bipolar disorder (22%), major depressive disorder (21%), and obsessive-compulsive disorder (OCD) (7%). Baseline cognitive impairment was observed in 96% of patients. After the third ECT session, there was a significant improvement in the naming domain ( P = 0.003) and a trend towards improvement in overall cognitive function ( P = 0.075). Patients with schizophrenia and bipolar disorder showed more significant improvements in cognitive function compared to those with other diagnoses. Education level was significantly associated with cognitive outcomes ( P = 0.004), with higher education correlating with better cognitive performance. Conclusion: This study suggests that ECT does not lead to significant cognitive decline in the short term and may improve certain cognitive domains, particularly naming ability. The cognitive effects of ECT vary across different psychiatric diagnoses and are influenced by educational background.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".