Should Benzodiazepines and Anticonvulsants Be Used During Electroconvulsive Therapy?
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate the clinical effects of benzodiazepines or anticonvulsant use during a course of electroconvulsive therapy (ECT). METHOD: A case report study of a patient who received ECT with and without concomitant flurazepam and pregabalin is presented. The literature on the use of benzodiazepines and anticonvulsants during ECT is reviewed. RESULTS: A woman with treatment resistant depression received a course of ECT while taking flurazepam and pregabalin, but seizures were of short duration and symptomatic improvement was minimal. After discontinuation of flurazepam and pregabalin, a course of right unilateral ultrabrief ECT was associated with adequate seizures and remission of depression and suicidal ideation. Our literature review suggests that benzodiazepines decrease seizure duration, but most evidence shows no association with increased seizure threshold. One prospective RCT and 3 large retrospective studies found that benzodiazepines compromise the efficacy of unilateral but not bilateral ECT. Regarding anticonvulsants, several studies had varied and contradictory results on their effect on seizure duration and seizure threshold. Of the 2 large retrospective studies and 3 RCTs, only 1 retrospective study showed that anticonvulsants decrease the efficacy of ECT. CONCLUSIONS: Judicious assessment of all medications used in combination with ECT is recommended. Overall, published studies suggest that benzodiazepines and anticonvulsants impact the clinical outcomes of ECT less than what would be expected given their pharmacologic effects. However, there are significant gaps in the literature, including a lack of study on suprathreshold stimulation of right unilateral ECT and the possibility of a greater effect with higher medication doses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it