Clinical Effectiveness of Electroconvulsive Therapy for Psychotic vs Nonpsychotic Depression
Bibliographic record
Abstract
Individuals experiencing major depression with psychotic features (MD-P) may respond better and have fewer cognitive effects with electroconvulsive therapy (ECT) than those without psychotic features (MD-NP). However, this may be due to differences in patient characteristics aside from psychotic symptoms. The objectives of this study were to (1a) compare ECT treatment response and (1b) adverse cognitive effects between patients with MD-P and MD-NP and (2a) explore factors associated with treatment response or (2b) adverse cognitive effects. This was a retrospective cohort study of adult inpatients with MD-P or MD-NP treated with an acute course of ECT at an academic psychiatric hospital June 2010-September 2021 in Toronto, Canada. Logistic regression was used to account for differences in patient characteristics between groups. Outcomes were identified using the clinical global impression improvement and cognitive function scales. > .05). However, after accounting for confounders, psychotic symptoms were not associated with response (adjusted odds ratio [AOR]: 1.04; 95% confidence interval [CI], 0.95-1.14) or adverse cognitive effects (AOR: 1.30; 95% CI, 0.78-2.18). Individuals with MD-P had a higher rate of response and similar rates of adverse cognitive effects compared to patients with MD-NP with ECT treatment. However, after accounting for differences in patient characteristics, we no longer identified an association between psychotic symptoms and treatment response.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".