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Record W1861683834 · doi:10.1586/14737175.2015.1083421

Novel targets and stimulation paradigms for deep brain stimulation

2015· review· en· W1861683834 on OpenAlexaff
Sol De Jesus, Leonardo Almeida, Zhongxing Peng-Chen, Michael S. Okun, Christopher W. Hess

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2015
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsDeep brain stimulationNeuroscienceBrain stimulationStimulationMovement disordersDiseaseMedicinePsychologyParkinson's disease

Abstract

fetched live from OpenAlex

Deep brain stimulation (DBS) is an accepted therapy for appropriately selected patients with movement disorders and psychiatric disease. The recent advances in lead technology and the advent of novel stimulation parameters have spurred a number of improvements that will likely be implemented in the clinical setting. Although the mechanisms and biology of DBS remain poorly understood, the progress in our understanding of network level dysfunction has driven the introduction of a variety of new targets and approaches to the treatment of human disease. Here we summarize the recent advances in novel stimulation patterns and customized field shaping. We also review new targets, novel applications of DBS and the immediate and long-term horizon for this therapy.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.136
GPT teacher head0.434
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations7
Published2015
Admission routes1
Has abstractyes

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