Repetitive Transcranial Magnetic Stimulation to Maintain Treatment Response to Electroconvulsive Therapy in Depression: A Case Series
Bibliographic record
Abstract
Electroconvulsive therapy (ECT) is the most effective treatment for a refractory major depression in the context of both unipolar and bipolar affective disorders. However, the relapse rate within the first 6 months after a successful course of ECT to treat a depressive episode can be as high 50%. Evidence-based strategies to prevent relapse have partial efficacy and are associated with problematic adverse effects limiting their use as long-term treatments. Repetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in treatment-resistant depression with a favorable adverse effect profile. Herein, we describe six patients, four with unipolar and two with bipolar depression, where rTMS was used to maintain response after a successful course of acute and continuation ECT. rTMS was administered once or twice weekly, at 120% of the resting motor threshold. Patients received sequential bilateral rTMS (low frequency right: 600 pulses, then high frequency left: 3000 pulses). The site of stimulation was 6 cm anterior and 1 cm lateral from the site of maximum stimulation of the abductor pollicis brevis muscle. Depressive symptoms were monitored with the quick inventory of depressive symptoms-self rated. Five of the six patients were able to maintain their response status from 6 to 13 months at the time of last observation. The use of rTMS may be an important relapse prevention strategy following an acute course of ECT. Controlled studies comparing rTMS to current evidence-based relapse prevention strategies are warranted.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".