The benefits and costs of changing treatment technique in electroconvulsive therapy due to insufficient improvement of a major depressive episode
Bibliographic record
Abstract
BACKGROUND: Electroconvulsive therapy (ECT) technique is often changed after insufficient improvement, yet there has been little research on switching strategies. OBJECTIVE: To document clinical outcome in ECT nonresponders who were received a second course using high dose, brief pulse, bifrontotemporal (HD BP BL) ECT, and compare relapse rates and cognitive effects relative to patients who received only one ECT course and as a function of the type of ECT first received. METHODS: Patients were classified as receiving Weak, Strong, or HD BP BL ECT during three randomized trials at Columbia University. Nonresponders received HD BP BL ECT. In a separate multi-site trial, Optimization of ECT, patients were randomized to right unilateral or BL ECT and nonresponders also received further treatment with HD BP BL ECT. RESULTS: Remission rates with a second course of HD BP BL ECT were high in ECT nonresponders, approximately 60% and 40% in the Columbia University and Optimization of ECT studies, respectively. Clinical outcome was independent of the type of ECT first received. A second course with HD BP BL ECT resulted in greater retrograde amnesia immediately, two months, and six months following ECT. CONCLUSIONS: In the largest samples of ECT nonresponders studied to date, a second course of ECT had marked antidepressant effects. Since the therapeutic effects were independent of the technique first administered, it is possible that many patients may benefit simply from longer courses of ECT. Randomized trials are needed to determine whether, when, and how to change treatment technique in ECT.
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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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".