Bilateral Sequential Theta Burst Stimulation for Multiple-Therapy-Resistant Depression: a naturalistic observation study
Bibliographic record
Abstract
Abstract Depression is a significant health issue with treatment resistance reported in about one third of patients. Treatment resistance results in significant disability, impaired quality of life, and increased healthcare costs. Repetitive transcranial magnetic stimulation (rTMS) is a treatment option for treatment resistant depression (TRD) with an average response rate of around 30%. Theta-burst is a novel rTMS paradigm that has shown promise as a treatment for TRD in some preliminary studies. In a naturalistic design, we evaluated the efficacy and tolerability of bilateral sequential (right then left) prefrontal theta-burst rTMS (bsTBS) in 50 patients with TRD (600 pulses/session, 20 sessions, 100% of resting motor threshold (two patients treated at 80% due to intolerance of 100%), F4/F3 of 10-20-20 EEG localization). Data was collected over 36 months from a specialized academic TMS clinic. Patients had multiple-treatment resistance with at least two failed trials of different antidepressants with 20% also having failed electroconvulsive therapy and 66% having received professional therapy. We found a 28% remission rate (HAMD-17 score of ≤ 7) and a 52% response rate (≥ 50% reduction in HAMD-17) with a 42% reduction in average HAMD-17 score. The treatment was well tolerated, with muscle contractions, mild pain or discomfort, headache, scalp irritation, and changes to vitals being captured as occasional adverse events with two instances of syncope (0.22% of treatments). This naturalistic study shows that bsTBS is a promising paradigm for a multiple-TRD patient population with approximately one-third of treatments achieving remission and over half achieving significant response. Previous Publication Abstract accepted and study presented at the 2018 Canadian Psychiatric Association Annual Conference, Toronto, Ontario, Canada, September 29, 2018.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".