Clinical Effectiveness and Cognitive Impact of Electroconvulsive Therapy for Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To determine the clinical effectiveness and cognitive impact of electroconvulsive therapy (ECT) in a large clinical sample of patients with schizophrenia and explore factors associated with treatment response and transient cognitive impairment. METHODS: We examined the clinical records of 144 patients with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder who were treated at an academic mental health hospital from October 2009 to August 2014. These patients received 171 acute courses of ECT; we attempted to determine their treatment response and transient cognitive impairment from ECT. We explored the impact of various factors including ECT indication, clinical characteristics, medication during ECT, and technical parameters on treatment response and transient cognitive impairment. RESULTS: Treatment with ECT resulted in a 76.7% response rate. Factors associated with a better response to ECT were absence of treatment with antiepileptic medication (17.9% vs 3.9%, P = .007), a previous good response to ECT (36.4% vs 15.4%, P = .017), and primary indication for ECT referral other than failed pharmacotherapy (89.7% vs 69.8%, P = .012). Factors not associated with treatment response included age, clozapine treatment, and benzodiazepine treatment (P > .05). Treatment with ECT caused transient cognitive impairment in 9% of treatment courses; no demographic or clinical factors were associated with cognitive impairment. CONCLUSIONS: This work demonstrates the effectiveness of ECT for schizophrenia treatment and several factors associated with treatment response. The rate of transient cognitive impairment is lower than expected based on the rate of cognitive impairment seen in ECT for depression. ECT appears to be an effective treatment option for schizophrenia that is tolerated by the majority of patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".