Clinical Characteristics of Inpatients with Schizophrenia Spectrum Disorder Treated with Electroconvulsive Therapy: A Population-Level Cross-Sectional Study: Titre: Caractéristiques cliniques des patients hospitalisés présentant un trouble du spectre de la schizophrénie et traités par électrochocs : Une étude de population transversale
Bibliographic record
Abstract
OBJECTIVE: Electroconvulsive therapy (ECT) is an evidence-based treatment for schizophrenia when anti-psychotic medications do not sufficiently control symptoms of psychosis or rapid response is required. Little is known about how it is used in routine clinical practice. The aim of this study was to identify the association of demographic and clinical characteristics with administration of ECT for schizophrenia spectrum disorders (SSD). METHODS: Among psychiatric inpatients with a diagnosis of SSD in Ontario, Canada (2006-2023), patient-level socio-demographic and clinical characteristics were described in those who did and did not receive ECT. We used multi-variable logistic regression to assess the association between patient-level characteristics and administration of ECT during index hospitalization. RESULTS: From 164,632 admissions, 2,168 (1.3%) involved exposure to ≥1 inpatient ECT procedure. Compared to those not receiving ECT, those receiving ECT were older, had higher rates of pre-admission medication use, medical and psychiatric comorbidities, outpatient mental health service use, but lower rates of substance use disorders. In the multi-variable logistic regression model, patient-level characteristics most strongly associated with receiving inpatient ECT were the presence of catatonia (odds ratio [OR]: 5.83; 95% confidence interval [95% CI]: 4.01-8.46), comorbid depression (OR: 2.49; 95% CI: 2.07-2.98), obsessive-compulsive disorder (OR: 2.16; 95% CI: 1.55-3.00), while characteristics most strongly associated with not receiving inpatient ECT were myocardial infarction (OR: 0.44; 95% CI: 0.20-0.95) and family conflict towards patient (OR: 0.47; 95% CI: 0.31-0.71). Neither severity of psychotic symptoms, non-command auditory hallucinations nor delusions were associated with administration of ECT. CONCLUSIONS: While characteristics associated with the use of ECT are generally consistent with the indications for ECT (e.g., catatonia, mood disorders), ECT is rarely used amongst individuals with SSD. Severity of psychotic symptoms was not associated with the use of inpatient ECT suggesting an opportunity to increase the use of ECT in this population. PLAIN LANGUAGE SUMMARY TITLE: Patient characteristics associated with receiving electroconvulsive therapy in schizophrenia and other psychotic illnesses.
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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 | 0.001 | 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 | 0.001 |
| 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".