Neuromodulatory treatments for psychiatric disease: A comprehensive survey of the clinical trial landscape
Bibliographic record
Abstract
BACKGROUND: Numerous neuromodulatory therapies are currently under investigation or in clinical use for the treatment of psychiatric conditions. OBJECTIVE/HYPOTHESIS: We sought to catalogue past and present human research studies on psychiatric neuromodulation and identify relevant trends in this field. METHODS: ClinicalTrials.gov (https://www.clinicaltrials.gov/) and the International Clinical Trials Registry Platform (https://www.who.int/ictrp/en/) were queried in March 2020 for trials assessing the outcome of neuromodulation for psychiatric disorders. Relevant trials were categorized by variables such as neuromodulation modality, country, brain target, publication status, design, and funding source. RESULTS: From 72,086 initial search results, 1252 unique trials were identified. The number of trials registered annually has consistently increased. Half of all trials were active and a quarter have translated to publications. The largest proportion of trials involved depression (45%), schizophrenia (18%), and substance use disorders (14%). Trials spanned 37 countries; China, the second largest contributor (13%) after the United States (28%), has increased its output substantially in recent years. Over 75% of trials involved non-convulsive non-invasive modalities (e.g., transcranial magnetic stimulation), while convulsive (e.g., electroconvulsive therapy) and invasive modalities (e.g., deep brain stimulation) were less represented. 72% of trials featured approved or cleared interventions. Characteristic inter-modality differences were observed with respect to enrollment size, trial design/phase, and funding. Dorsolateral prefrontal cortex accounted for over half of focal neuromodulation trial targets. The proportion of trials examining biological correlates of neuromodulation has increased. CONCLUSION(S): These results provide a comprehensive overview of the state of psychiatric neuromodulation research, revealing the growing scope and internationalism of this field.
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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 | 0.074 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".