Evaluation of a Novel Therapeutic Repetitive Transcranial Magnetic Stimulation Technique Optimized for Increased Accessibility in Major Depression
Bibliographic record
Abstract
ABSTRACT BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) is effective in major depressive disorder (MDD). However, technical complexity and operational costs might have been barriers for its wide use and implementation in some jurisdictions, thereby decreasing accessibility. OBJECTIVE Our main goal was to test the feasibility of a novel rTMS protocol optimized for practicality, scalability and cost-effectiveness. We hypothesized that our novel rTMS protocol would be simple to implement and well-tolerated, but less costly and allow for more treatment capacity. METHODS Treatment was administered in an open-room setting, allowing a single technician to attend to multiple patients. Large non-focal parabolic coils held by custom-built arms allowed simple yet efficient and accurate placement. We employed a low-frequency (LF) 1 Hz stimulation protocol (360 pulses per session), delivered on the most affordable FDA-approved devices. MDD participants received an initial accelerated rTMS course (arTMS) of 6 sessions/day over 5 days (30 total), followed by a tapering course of daily sessions (up to 25) to decrease the odds of relapse. The self-reported Beck Depression Inventory II (BDI-II) was used to measure severity of depression. RESULTS Forty-eight (48) patients completed the arTMS course. No serious adverse events occurred, and all patients reported manageable pain levels. Response and remission rates were 35.4% and 27.1% on the BDI-II, respectively, at the end of the tapering course. CONCLUSION If rTMS could be delivered for lower cost at higher volume, while preserving efficacy, safety and tolerability, it could warrant further investigation of this treatment as a first-line intervention in MDD. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT04376697
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".