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Record W3197139576 · doi:10.1016/j.brs.2021.08.021

Neuromodulatory treatments for psychiatric disease: A comprehensive survey of the clinical trial landscape

2021· article· en· W3197139576 on OpenAlexafffund
Gavin J.B. Elias, Alexandre Boutet, Roohie Parmar, Emily H.Y. Wong, Jürgen Germann, Aaron Loh, Michelle Paff, Aditya Pancholi, Dave Gwun, Clement T. Chow, Flavia Venetucci Gouveia, Irene E. Harmsen, Michelle E. Beyn, Emiliano Santarnecchi, Alfonso Fasano, Daniel M. Blumberger, Sidney H. Kennedy, Andrés M. Lozano, Venkat Bhat

Bibliographic record

VenueBrain stimulation · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsSt. Michael's HospitalCentre for Addiction and Mental HealthHealth Sciences CentreToronto Rehabilitation InstituteSunnybrook Health Science CentreKrembil FoundationUniversity of TorontoUniversity Health Network
FundersDepartment of Psychiatry, University of TorontoDefense Advanced Research Projects AgencyServierUniversity of TorontoOtsuka AmericaBrainsWayBoston Scientific CorporationCanadian Institutes of Health ResearchSunovionCentre for Addiction and Mental Health FoundationAbbott LaboratoriesFondation Brain CanadaAssociation for Frontotemporal DegenerationMinistry of Health -SingaporeBeth Israel Deaconess Medical CenterPfizerAlzheimer's Drug Discovery FoundationNational Institutes of HealthMedtronic
KeywordsNeuromodulationClinical trialMedicinePsychiatryElectroconvulsive therapyMajor depressive disorderModalitiesSchizophrenia (object-oriented programming)BlindingDeep brain stimulationPsychological interventionRandomized controlled trialDiseaseInternal medicineMoodParkinson's disease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.074
metaresearch head score (Gemma)0.146
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.146
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0140.025
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.134
GPT teacher head0.401
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations36
Published2021
Admission routes2
Has abstractyes

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