Cognitive Functioning in Patients with Schizophrenia on Long-term Maintenance Electroconvulsive Therapy – A Case Series
Bibliographic record
Abstract
Schizophrenia is a complex disorder with heterogeneous course with the majority of patients having multiple relapses. Electroconvulsive therapy (ECT) when combined with antipsychotics is a potential option in preventing relapse of psychotic symptoms. The cumulative effects of long-term maintenance ECT on cognitive functions in schizophrenia are unknown. We aim to report the cognitive profile of patients with schizophrenia who received long-term maintenance electroconvulsive therapy (M-ECT). The socio-demographic details, illness characteristics, details about ECT were obtained from retrospective file review. The scores on the Hindi Mental Status Examination (HMSE) were compared before and after the latest session of M-ECT. After obtaining written informed consent, cognitive functions were evaluated in detail by using Montreal Cognitive Assessment (MoCA) and Battery for ECT Related Cognitive Deficits (B4ECT-ReCoDe) which is a specific tool to assess cognitive functions that are affected by ECT. 5 patients (2 male and 3 female) with a mean age of 44.2 years (SD 8.5) received M-ECT over a period of 8 years. There was an improvement in the overall functioning of patients. Verbal episodic memory, visual memory and working memory were the affected cognitive domains with preserved processing speed, sustained attention, autobiographical memory, and global cognitive functioning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".