Comparative Efficacy of Augmenting Escitalopram with Modified Electroconvulsive Therapy or High-Frequency Repetitive Transcranial Magnetic Stimulation on Depressive Symptoms, Quality of Life, and Cognitive Function in Treatment-Resistant Depression
Bibliographic record
Abstract
Treatment-resistant depression (TRD) poses significant therapeutic challenges despite available interventions. Escitalopram (ESC) is a highly selective antidepressant. This study aimed to compare ESC alone and ESC combined with modified electroconvulsive therapy (MECT) or high-frequency repetitive transcranial magnetic stimulation (HF-rTMS) in TRD patients. Ninety participants were randomized into ESC alone, ESC + MECT, and ESC + HF-rTMS groups. Notable differences were observed in Hamilton Depression Rating Scale (HDRS-17) scores at 12 weeks among ESC (14.37), ESC + MECT (10.27), and ESC + HF-rTMS (10.77) groups (P = 0.006). In terms of overall quality of life (QoL) evaluated using the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF) at 12 weeks, the ESC, ESC + MECT, and ESC + HF-rTMS groups scored 2, 3, and 3.5, respectively. ESC + MECT/HF-rTMS groups showed reduced depressive symptoms compared to the ESC group, accompanied by higher overall QoL scores and increased satisfaction with health. Patients receiving ESC + MECT demonstrated no significant alterations in short-term memory and orientation, as measured by the Montreal Cognitive Assessment (MoCA), before and after treatment. Moreover, a decline in language was observed compared to baseline (12 weeks: median 2, IQR 2-3; baseline: median 1, IQR 1-3; P = 0.022). The positive impact of ESC with HF-rTMS on cognitive function was evidenced by improvements in all domines MoCA.Combining ESC with MECT or HF-rTMS exhibited enhanced effectiveness in alleviating depressive symptoms and enhancing QoL compared to ESC monotherapy. Specifically, the ESC + HF-rTMS combination displayed potential as a comprehensive treatment strategy for TRD, addressing both emotional and cognitive aspects.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".