Clinical Effectiveness of Maintenance Electroconvulsive Therapy in Patients with Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess the clinical effectiveness and cognitive effects of maintenance electroconvulsive therapy (mECT) in patients with schizophrenia or schizoaffective disorder and explore factors associated with both outcomes. METHODS: In this retrospective cohort study, we examined clinical records of 47 patients with a Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) diagnosis of schizophrenia or schizoaffective disorder treated with mECT at an academic mental health hospital between April 2010 and July 2016. Sixty-two mECT courses were reviewed. We assessed clinical effectiveness and cognitive effects as well as factors associated with response to treatment, including psychiatric diagnosis, concomitant pharmacological treatment, and previous treatment response. RESULTS: Maintenance electroconvulsive therapy was able to maintain clinical response in 48 (77%) treatment courses. Significant cognitive adverse effects were reported in 7 (11%) of the courses. Use of antipsychotic, antidepressant or benzodiazepine medications, psychiatric disorder, and sex were not associated with response. CONCLUSION: This study shows meaningful clinical effectiveness and good tolerability of mECT in patients with resistant schizophrenia over extended periods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".