A Pilot Study of Cognitive Impairment in Longstanding Electroconvulsive Therapy–treated Schizophrenia Patients Versus Controls
Bibliographic record
Abstract
BACKGROUND: Electroconvulsive therapy (ECT), though reliable and effective, is controversial due to its media portrayal as a treatment with severe side effects. Electroconvulsive therapy is mainly given to patients suffering from affective disorders and treatment-resistant schizophrenia. Although past research assessed the amount and duration of memory loss due to ECT, little is known about its influence on cognition for patients suffering from schizophrenia, whose cognitive decline is an inherent part of their illness. We aimed to test whether maintenance ECT causes cognitive decline among elderly schizophrenia patients. METHODS: Twenty elderly (age >65 years) patients suffering from schizophrenia and schizoaffective disorder who received maintenance ECT were matched with 20 controls suffering from the same illnesses that have never been treated with ECT. The match was based on age, sex, and illness duration. The participants were evaluated using the Montreal Cognitive Assessment for cognitive decline and a Positive and Negative Syndrome Scale (PANSS) for illness severity. RESULTS: A lower score in the abstraction subscale was found in the maintenance ECT population (P = 0.002), without significant differences in the total Montreal Cognitive Assessment and the delayed-recall subscale scores. In the treatment group, a correlation was found between an impairment in naming and positive symptoms in the PANSS score (r = -0.45) and between abstraction impairment and negative symptoms (r = -0.56) and total PANSS score (r = -0.497). CONCLUSIONS: Maintenance ECT does not worsen existing global cognitive deficits or delayed recall in elderly schizophrenia patients. The abstraction impairment was possibly due to the higher disease burden of the patients referred to ECT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".