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Record W3045291114 · doi:10.3390/brainsci10080480

What Do We Know about Transcranial Direct Current Stimulation for Major Depression?

2020· editorial· en· W3045291114 on OpenAlexaff
Derrick Matthew Buchanan, Philippe Robaey, Amedeo D’Angiulli

Bibliographic record

VenueBrain Sciences · 2020
Typeeditorial
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of OttawaCarleton UniversityChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsTranscranial direct-current stimulationDepression (economics)Physical medicine and rehabilitationCurrent (fluid)StimulationPsychologyNeuroscienceMedicineAudiologyPhysicsEconomics

Abstract

fetched live from OpenAlex

The interest in using non-invasive brain stimulation (NIBS) for the treatment of major depression (MD), including treatment resistant depression, is growing rapidly. The paper by Bennabi and Haffen (Brain Sci. 2018, 8) was an important step towards the formal acceptance of transcranial direct current stimulation (tDCS) as a possible form of therapy. Their review demonstrated favourable support for the beneficial effects of tDCS for MD, coupled with necessary practical considerations, such as its relatively low cost, portability/ease of use in clinical settings, non-invasiveness, and good tolerability. Here, we provide a follow-up to their review and sketch a current update. Means for optimizing tDCS efficacy and potential limitations of current studies are discussed.

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)0.005
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.001
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0050.005

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 head0.047
GPT teacher head0.358
Teacher spread0.311 · 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
GenreEditorial

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
Published2020
Admission routes1
Has abstractyes

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