Predictors of cognitive changes in patients with schizophrenia undergoing electroconvulsive therapy
Bibliographic record
Abstract
INTRODUCTION: Previous studies on the effects of electroconvulsive therapy (ECT) on cognition in schizophrenia have been inconclusive. This study aimed to identify factors that may predict cognitive improvement or deterioration in patients with schizophrenia after-ECT. MATERIALS & METHODS: Patients with schizophrenia or schizoaffective disorder with predominantly positive psychotic symptoms, who were treated with ECT at the Institute of Mental Health (IMH), Singapore, between January 2016 and January 2018, were assessed. Montreal Cognitive Assessment (MoCA), Brief Psychiatric Rating Scale (BPRS) and Global Assessment of Function (GAF) were performed before and after ECT. Patients with clinically significant improvement, deterioration or no change in MoCA scores were compared on demographics, concurrent clinical treatment and ECT parameters. RESULTS: Of the 125 patients analysed, 57 (45.6%), 36 (28.8%) and 32 (25.6%) showed improvements, deterioration and no change in cognition respectively. Age and voluntary admission predicted MoCA deterioration. Lower pre-ECT MoCA and female sex predicted MoCA improvement. Patients showed improvements in GAF, BPRS and BPRS subscale scores on average, except for the MoCA deterioration group, who did not show statistically significant improvement in negative symptom scores. Sensitivity analysis showed that nearly half the patients (48.3%) who were initially unable to complete MoCA pre-ECT were able to complete MoCA post-ECT. CONCLUSIONS: The majority of patients with schizophrenia demonstrate improved cognition with ECT. Patients with poor cognition pre-ECT are more likely to see improvement post-ECT. Advanced age may be a risk factor for cognitive deterioration. Finally, improvements in cognition may be associated with improvements in negative symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".