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Record W1986341987 · doi:10.3109/09540261.2011.630993

Neurostimulation therapies for treatment resistant depression: A focus on vagus nerve stimulation and deep brain stimulation

2011· review· en· W1986341987 on OpenAlexaff
Sakina J. Rizvi, Madelin Donovan, Peter Giacobbe, Franca Placenza, Susan Rotzinger, Sidney H. Kennedy

Bibliographic record

VenueInternational Review of Psychiatry · 2011
Typereview
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsVagus nerve stimulationNeurostimulationDeep brain stimulationDepression (economics)Treatment-resistant depressionAntidepressantMedicinePharmacotherapyMajor depressive disorderStimulationElectroconvulsive therapyBrain stimulationPopulationVagus nervePsychiatryAnesthesiaInternal medicineDiseaseParkinson's diseaseSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

Antidepressant treatments, including pharmacotherapy and psychotherapy, do not result in remission for the majority of patients with major depressive disorder. The high prevalence of treatment resistant depression (TRD) poses a significant issue for patients as well as both societal and economic costs. Due to the limited efficacy of existing therapies in this sub-population, alternative somatic treatments are being explored. Both vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are neurostimulation treatments for TRD. While VNS has Food Drug Administration approval as an adjunctive therapy for MDD, DBS is still in the experimental stages. This article will review the evidence supporting the clinical utility of these therapies.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.103
GPT teacher head0.420
Teacher spread0.317 · 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

Citations42
Published2011
Admission routes1
Has abstractyes

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