Impacts of Switching Antidepressants After Successful Electroconvulsive Therapy on the Maintenance of Clinical Remission in Patients With Treatment-Resistant Depression
Bibliographic record
Abstract
INTRODUCTION: There is no consensus regarding whether a previously prescribed, that is, failed, antidepressant should be continued or switched after a successful electroconvulsive therapy (ECT) for the maintenance of clinical remission in patients with treatment-resistant depression (TRD). In this study, we conducted a chart review to examine impacts of the antidepressant switch after the successful ECT on 1-year outcome in patients with TRD. MATERIALS AND METHODS: This retrospective chart review included inpatients with TRD (ie, those who failed to respond to adequate trials of 2 distinctly different classes of antidepressants) who showed clinical remission after ECT. Readmission rate and social functioning 6 months and 1 year after the successful ECT were compared between patients who experienced an antidepressant switch and those who continued prior regimen. RESULTS: Twenty-eight patients (mean age, 59 years; 9 men) were followed-up for 1 year. The patients who changed antidepressants after ECT (n = 7) experienced a readmission significantly less frequent than the others (n = 21) in 1 year (0% vs 43%, P = 0.043). In addition, the former showed significantly better social contacts at 6 months (P = 0.022) and 1 year (P = 0.015). There were no significant differences in baseline characteristics between the 2 groups. CONCLUSIONS: The patients who experienced an antidepressant switch after ECT required a readmission less frequently in 1 year than those who were maintained with the same antidepressant. The findings of this preliminary study suggest that a switch to another antidepressant after successful ECT may be encouraged for the maintenance of clinical remission in patients with TRD.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".