Repetitive Transcranial Magnetic Stimulation with and without Internet-Delivered Cognitive Behavior Therapy for the Treatment of Resistant Depression: Patient-centered Randomized Controlled Pilot Trial
Bibliographic record
Abstract
INTRODUCTION: BACKGROUND: Treatment-resistant depression (TRD) is considered one of the major clinical challenges in the field of psychiatry. At least 15% of all patients with MDD remain refractory to any treatment intervention. Repetitive transcranial magnetic stimulation (rTMS) is considered a treatment option for patients with TRD. Additionally, iCBT is an evidence-based psychotherapy for the management of TRD. OBJECTIVES: This study aims to evaluate the clinical effectiveness of adding iCBT to rTMS treatment as an innovative combined intervention, exploring the short and long-term outcomes on patients with TRD METHODS: This study is a randomized controlled trial. Participants diagnosed with TRD were randomized to one of two interventions: rTMS alone and rTMS+iCBT. Each group completed evaluation measures at baseline, discharge (6 weeks), and one & three months after discharge. The primary outcome measure was the mean change in the Hamilton depression rating scale (HAMD-17) from baseline to three months. RESULTS: Preliminary results for the early outcome of the study showed that after adjusting for the baseline scores, there was no significant difference in the mean score of HAMD-17 from baseline to six weeks between the participants of the two groups, (F (1, 53) = 0.15, p = 0.70, partial eta square = 0.003). The result of the long-term effectiveness is underway, forecasting the potential synergism of the two interventions. CONCLUSIONS: This study found the combined treatment of rTMS + iCBT not to be superior to treatment with rTMS alone, in the short term. We hope the long-term results would thoroughly address the effectiveness of the combined therapy in this randomized controlled trial. DISCLOSURE OF INTEREST: None Declared
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".