Consensus review and considerations on TMS to treat depression: A comprehensive update endorsed by the National Network of Depression Centers, the Clinical TMS Society, and the International Federation of Clinical Neurophysiology
Bibliographic record
Abstract
• Repetitive transcranial magnetic stimulation (rTMS) is a safe and effective treatment for depressive disorders. • New forms of rTMS such as intermittent theta burst (iTBS) are non-inferior to standard rTMS for treating depression. • Attempts to enhance efficacy (accelerated iTBS, MRI guidance, neuroplasticity modulators) show promising early findings. This article updates the prior 2018 consensus statement by the National Network of Depression Centers (NNDC) on the use of transcranial magnetic stimulation (TMS) in the treatment of depression, incorporating recent research and clinical developments. Publications on TMS and depression between September 2016 and April 2024 were identified using methods informed by PRISMA guidelines. The NNDC Neuromodulation Work Group met monthly between October 2022 and April 2024 to define important clinical topics and review pertinent literature. A modified Delphi method was used to achieve consensus. 2,396 abstracts and manuscripts met inclusion criteria for review. The work group generated consensus statements which include an updated narrative review of TMS safety, efficacy, and clinical features of use for depression. Considerations related to training, roles/responsibilities of providers, and documentation are also discussed. TMS continues to demonstrate broad evidence for safety and efficacy in treating depression. Newer forms of TMS are faster and potentially more effective than conventional repetitive TMS. Further exploration of targeting methods, use in special populations, and accelerated protocols is encouraged. This article provides an updated overview of topics relevant to the administration of TMS for depression and summarizes expert, consensus opinion on the practice of TMS in the United States.
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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 | 0.113 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".