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Record W2323868350 · doi:10.3389/fnins.2016.00119

Proceedings of the Third Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies

2016· review· en· W2323868350 on OpenAlexaff
P. Justin Rossi, Aysegul Gunduz, Jack W. Judy, Linda Wilson, André G. Machado, James Giordano, W. Jeff Elias, Marvin A. Rossi, Christopher L. Butson, Michael Fox, Cameron C. McIntyre, Nader Pouratian, Nicole C. Swann, Coralie de Hemptinne, Robert E. Gross, H.J. Chizeck, Michele Tagliati, Andrés M. Lozano, Wayne K. Goodman, Jean‐Philippe Langevin, Ron L. Alterman, Umer Akbar, Greg A. Gerhardt, Warren M. Grill, Mark Hallett, Todd M. Herrington, Jeffrey A. Herron, Craig van Horne, Brian H. Kopell, Anthony E. Lang, Codrin Lungu, Daniel Martínez-Ramírez, Alon Y. Mogilner, Rene Molina, Enrico Opri, Kevin J. Otto, Karim Oweiss, Yagna Pathak, Aparna Wagle Shukla, J Shute, Sameer A. Sheth, Ludy C. Shih, G. Karl Steinke, Alexander I. Tröster, Nora Vanegas, Kareem A. Zaghloul, Leopoldo Cendejas‐Zaragoza, L. Verhagen, Kelly D. Foote, Michael S. Okun

Bibliographic record

VenueFrontiers in Neuroscience · 2016
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and Stroke
KeywordsDeep brain stimulationMultidisciplinary approachEngineering ethicsNeuroscienceNeurologic diseaseMedicineData sciencePsychologyComputer scienceEngineeringDiseasePolitical sciencePathology

Abstract

fetched live from OpenAlex

The proceedings of the 3rd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, imaging, and computational work on DBS for the treatment of neurological and neuropsychiatric disease. Significant innovations of the past year are emphasized. The Think Tank's contributors represent a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers, and members of industry. Presentations and discussions covered a broad range of topics, including policy and advocacy considerations for the future of DBS, connectomic approaches to DBS targeting, developments in electrophysiology and related strides toward responsive DBS systems, and recent developments in sensor and device technologies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.323
Teacher spread0.301 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations38
Published2016
Admission routes1
Has abstractyes

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