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Record W4391671643 · doi:10.1016/j.brs.2024.01.008

Identifying clinical predictors of response to repetitive transcranial magnetic stimulation for smoking cessation: Secondary analysis of a multicenter RCT

2024· letter· en· W4391671643 on OpenAlexaffabout
Victor M. Tang, Laurie Zawertailo, Peter Selby, Abraham Zangen, Dhvani Mehta, Tony P. George, Bernard Le Foll, Kristina M. Gicas, Matthew E. Sloan, Scott Veldhuizen

Bibliographic record

VenueBrain stimulation · 2024
Typeletter
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of the Fraser ValleyPublic Health OntarioUniversity of TorontoWaypoint Centre for Mental Health CareCentre for Addiction and Mental Health
FundersBrainsWay
KeywordsRandomized controlled trialTranscranial magnetic stimulationMedicineSmoking cessationNicotineDeep transcranial magnetic stimulationPsychiatryInternal medicineStimulation

Abstract

fetched live from OpenAlex

Tobacco smoking remains a leading cause of morbidity and mortality worldwide. While evidence-based treatments exist for nicotine dependence, many patients do not respond to or cannot tolerate them. Repetitive Transcranial Magnetic Stimulation (rTMS) is a novel, non-invasive neuromodulation treatment that stimulates parts of the brain involved in addiction [1]. Recently, the Brainsway H4 deep TMS coil was cleared by the FDA and Health Canada for the treatment for nicotine dependence, based on a pivotal multisite RCT with continuous quit rates of 19.4 % from rTMS compared to 8.7 % in the sham control group [2].

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)0.018
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.014
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.074
GPT teacher head0.370
Teacher spread0.296 · 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 designNon-randomized trial
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

Citations0
Published2024
Admission routes2
Has abstractyes

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