Effects of Binaural Beat Music and Esketamine for ECT in the Treatment of Major Depressive Disorder: A Randomized Controlled Trial Protocol
Bibliographic record
Abstract
Purpose: Major depressive disorder (MDD) has a high incidence and high risk of suicide. Electroconvulsive therapy (ECT) is a highly effective and rapid physical therapy for MDD but limited by adverse effects. Ketamine/esketamine are new emerging rapid antidepressants, which can synergize the efficacy and safety of ECT but come with other side effects. Binaural beat music (BBM) is a non-invasive somatopsychic therapy with the potential to improve mood and assist the efficacy of (es)ketamine. Purpose of this study is to investigate the modification of BBM on effects of (esketamine combined with) ECT for MDD, and the probable interaction between BBM and esketamine, aiming to provide insights for optimizing ECT and improving outcomes of patients with MDD. Patients and Methods: This study is a 2×2 factorial, prospective, randomized, controlled, blinded clinical trial that recruiting 476 patients with MDD who require ECT treatments. These participants are randomly 1:1:1:1 allocated to the following groups (119 in each group): ① Group B0E0 (blank sound and normal saline); ② Group B0E1 (blank sound and esketamine); ③ Group B1E0 (BBM and normal saline); ④ group B1E1 (BBM and esketamine). The primary outcome is the response rate of patients to ECT treatment, assessed using the Hamilton depression scale (HAMD). Secondary outcomes include remission rate of depression and remission of suicidal ideation (assessed using HAMD), accompanied psychotic symptoms assessed using the Brief Psychiatric Rating Scale, cognitive function assessed using the Montreal Cognitive Assessment Scale, parameters of ECT, perianesthesia vital signs and anesthesia-related indices, blood biomarkers, and side effects. Discussion: This study provides the first clinical evidence of the effects of BBM alone or interacted with esketamine in patients with MDD undergoing ECT. Our data are expected to suggest BBM's potential for developing better ECT therapeutic strategies, optimizing treatments for MDD and promoting prognosis.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".