Treatment Capacity and Clinical Outcomes for Patients With Schizophrenia Who Were Treated With Electroconvulsive Therapy: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Patients with schizophrenia are often found incapable to consent to psychiatric treatment. We evaluated clinical outcomes for incapable and capable patients with schizophrenia treated with electroconvulsive therapy (ECT). METHODS: We conducted a chart review of all inpatients treated with an acute course of ECT between 2010 and 2018 at the Centre for Addiction and Mental Health, Toronto, Canada. Short-term outcomes included treatment response and cognitive impairment. We assessed whether incapable patients regained the capacity to consent to treatment. Long-term outcomes included readmissions and subsequent courses of acute or maintenance ECT. RESULTS: A total of 159 (67%) incapable and 79 (33%) capable patients were included. Patients experienced treatment response (incapable, n = 108, 67.9%; capable, n = 52, 65.8%; P = .771) and few experienced cognitive impairment (incapable, n = 21, 13.2%; capable, n = 19, 24.1%; P = .043). A minority of patients were treated with a subsequent course of acute ECT (incapable, n = 46, 28.9%; capable, n = 16, 20.3%; P = .162). Incapable patients were more likely to be treated with maintenance ECT for at least 6 months (incapable, n = 46, 28.9%; capable, n = 13, 16.5%; P = .039). Both groups had similar readmission rates (incapable, n = 70, 44.0%; capable, n = 35, 44.3%; P = 1.000). Eight (5.0%) incapable patients regained capacity and 7 consented to further treatment. CONCLUSIONS: Irrespective of treatment capacity, the majority of patients demonstrated clinical improvement. Incapable patients experienced less cognitive side effects when compared with capable patients, though they had fewer treatments overall. This study informs clinicians, patients, and substitute decision-makers about the outcomes and challenges of ECT in patients with schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".