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Record W2150822776 · doi:10.3109/09540261.2011.618827

Repetitive transcranial magnetic stimulation and drug addiction

2011· review· en· W2150822776 on OpenAlexafffund
Mera S. Barr, Faranak Farzan, Victoria C. Wing, Tony P. George, Paul B. Fitzgerald, Zafiris J. Daskalakis

Bibliographic record

VenueInternational Review of Psychiatry · 2011
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute on Drug AbuseCanadian Institutes of Health Research
KeywordsTranscranial magnetic stimulationAddictionDorsolateral prefrontal cortexMedicineBrain stimulationPsychologyDrugPsychiatryCravingStimulationNeurosciencePrefrontal cortexCognition

Abstract

fetched live from OpenAlex

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique that is now being tested for its ability to treat addiction. This review discusses current research approaches and results of studies which measured the therapeutic use of rTMS to treat tobacco, alcohol and illicit drug addiction. The research in this area is limited and therefore all studies evaluating the therapeutic use of rTMS in tobacco, alcohol or illicit drug addiction were retained including case studies through NCBI PubMed ( http://www.ncbi.nlm.nih.gov ) and manual searches. A total of eight studies were identified that examined the ability of rTMS to treat tobacco, alcohol and cocaine addiction. The results of this review indicate that rTMS is effective in reducing the level of cravings for smoking, alcohol, and cocaine when applied at high frequencies to the dorsolateral prefrontal cortex (DLPFC). Furthermore, these studies suggest that repeated sessions of high frequency rTMS over the DLPFC may be most effective in reducing the level of smoking and alcohol consumption. Although work in this area is limited, this review indicates that rTMS is a promising modality for treating drug addiction.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.340
Teacher spread0.299 · 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.

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

Citations79
Published2011
Admission routes2
Has abstractyes

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