Pharmacotherapy Prescriptions for Relapse Prevention of Psychotic Depression After Electroconvulsive Therapy
Bibliographic record
Abstract
PURPOSE/BACKGROUND: Electroconvulsive therapy (ECT) is effective in the treatment of acute episodes of psychotic depression. However, no adequately powered studies have directly investigated the efficacy of antipsychotic pharmacotherapy in relapse prevention of psychotic depression after ECT. In the absence of such literature, we reviewed the clinical practice of 4 academic medical centers that have made research contributions in the treatment of psychotic depression over the past 20 years. METHODS/PROCEDURES: We reviewed medical records of patients with a diagnosis of psychotic depression who received 1 or more acute courses of ECT over the span of 3 years. Chi-square tests were used to compare pharmacotherapy prescribed at the time of completion of ECT. FINDINGS/RESULTS: A total of 163 patients received 176 courses of ECT for separate episodes of psychotic depression. The combination of an antidepressant plus an antipsychotic was the most common regimen, ranging from 61.9% to 85.5% of all prescriptions. One center added lithium in 45.5% of cases treated with the combination of an antidepressant plus an antipsychotic. An antipsychotic alone was prescribed in less than 10% of cases. An antidepressant alone or other drug combinations were rare. IMPLICATIONS/CONCLUSIONS: The combination of an antidepressant plus an antipsychotic was the most commonly prescribed regimen at the completion of ECT for relapse prevention in patients with psychotic depression acutely treated with ECT. Although this report offers a view of the clinical practice of 4 academic medical centers, it also points to the need of randomized controlled trials on continuation pharmacotherapy after treatment of psychotic depression with ECT.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".