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Record W1995782021 · doi:10.1126/scitranslmed.3003722

Deep Brain Stimulation for Psychiatric Disease: Contributions and Validity of Animal Models

2012· review· en· W1995782021 on OpenAlexaff
Clement Hamani, Yasin Temel

Bibliographic record

VenueScience Translational Medicine · 2012
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalCentre for Addiction and Mental Health
Fundersnot available
KeywordsDeep brain stimulationDystoniaNeuroscienceBrain stimulationMedicineDiseaseStimulationEssential tremorCognitionPsychiatric DiseaseParkinson's diseasePsychologyPathology

Abstract

fetched live from OpenAlex

During treatment with deep brain stimulation (DBS), electrical current is delivered into the brain parenchyma through implanted electrodes. Although this technique is routinely used in the treatment of Parkinson's disease, essential tremor, and dystonia, a growing number of neuropsychiatric applications for DBS are being investigated. Investigators can use animal models of these diseases to study the mechanisms through which DBS exerts its effects, to explore new applications of this therapy, and to identify and characterize alternative stimulation targets. Here, we discuss preclinical DBS research that provides insight into the mechanisms underlying cognitive and psychiatric applications of this technique, emphasizing the predictive validity of animal models and their potential use in translational research.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.915
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.145
GPT teacher head0.420
Teacher spread0.275 · 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

Citations149
Published2012
Admission routes1
Has abstractyes

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