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Record W3033548177 · doi:10.1016/j.ebiom.2020.102809

The effects of repetitive transcranial magnetic stimulation on cue-induced craving in male patients with heroin use disorder

2020· article· en· W3033548177 on OpenAlexafffund
Xiaoli Liu, Xiwen Zhao, Ting Liu, Qingming Liu, Li Tang, Hui Zhang, Wenbo Luo, Zafiris J. Daskalakis, Ti‐Fei Yuan

Bibliographic record

VenueEBioMedicine · 2020
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsTranscranial magnetic stimulationCravingHeroinStimulationMedicineDeep transcranial magnetic stimulationNeurosciencePsychiatryHeroin dependenceAudiologyPsychologyAnesthesiaClinical psychologyPhysical medicine and rehabilitationAddictionDrug

Abstract

fetched live from OpenAlex

BACKGROUND: Craving is a central feature of addiction. Early evidence suggests that repetitive transcranial magnetic stimulation is effective in reducing cue induced craving for patients with opioid use disorder (OUD). However, trials in large populations of patients with OUDs are lacking. METHODS: We randomly assigned 118 male heroin patients into three groups (i.e., 10 Hz rTMS, 1 Hz rTMS and a wait-list control group) from two addiction rehabilitation centers. rTMS was applied to the left dorsolateral prefrontal cortex (DLPFC) for 20 daily consecutive sessions. FINDINGS: Results showed that 10 Hz rTMS and 1 Hz rTMS were both effective in reducing cue-induced craving scores in heroin users when compared to the wait list group. The treatment effects lasted for up to 60 days after rTMS treatment cessation. INTERPRETATION: Our results suggest that rTMS applied to the DLPFC is effective in reducing craving severity in heroin use disorder patients. Our results also suggest that such treatment effects can last for up to 60 days after treatment cessation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.239
Teacher spread0.222 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations64
Published2020
Admission routes2
Has abstractyes

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