Towards accredited clinical training in brain stimulation: Proceedings from the brain stimulation subspecialty summits
Bibliographic record
Abstract
The rapid development and clinical use of brain stimulation has renewed debates about whether to define and accredit a pathway for clinical subspecialty training. To address this, the Brain Stimulation Subspecialty Summits (BraSSS) were convened in 2023 and 2024, featuring international leaders in brain stimulation across psychiatry, neurology, neurosurgery, psychology, and neuroscience. Both meetings included two days of lectures and debates focused on clinical content, emerging science, and educational standards. The 2023 meeting was held at Brigham & Women's Hospital and Harvard University, where 54 attendees reached a consensus that the subspecialty is adequately developed to warrant formal recognition and initiated debates regarding the name and scope of the subspecialty. The 2024 meeting was held at Stanford University, where 56 attendees developed a content outline, organized committees, and reached a consensus to form an independent society focused on developing and maintaining unbiased accreditation standards. "Brain stimulation" was chosen democratically as the name of the subspecialty. Clinicians from multiple primary specialties may enter this subspecialty training track. While individual programs may have a specific area of focus (e.g. interventional psychiatry or epilepsy), our expectation is that accredited brain stimulation programs will provide training experiences that cross specialties and stimulation modalities. Several potential unintended consequences were discussed, and plans were developed to address them. Overall, subspecialty recognition was deemed to be beneficial to the brain stimulation field, with a goal to launch an associated society and start the process of accrediting existing US and Canadian programs in 2025.
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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 | 0.086 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.017 | 0.033 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".