Insula deep rTMS and varenicline for smoking cessation: A randomized control trial study protocol
Bibliographic record
Abstract
Background: Current approved therapies for smoking cessation have modest long-term effects for abstinence. The insular cortex has been identified by preclinical and clinical studies as a critical target for addiction treatment. Insula functions can be modulated non-invasively using brain stimulation. It is unknown if deep repetitive transcranial magnetic stimulation (rTMS) of the insula can improve smoking cessation of smokers trying to quit using varenicline. Methods: This will be a randomized, double-blind, sham-controlled clinical trial with 50 nicotine dependent smokers looking to quit. They will be randomly assigned to receive either active (10 Hz) or sham insula deep rTMS. Deep rTMS will be administered for 4 weeks (5 days/week). All participants will receive open label varenicline for 12 weeks. The primary outcome measure will be the 7-day point prevalence abstinence at the end of 12 weeks. The secondary outcomes will be Fagerström Test of Nicotine Dependence, Minnesota Nicotine Withdrawal Scale, Tiffany Questionnaire of Smoking Urges, expired carbon monoxide measurements, cigarettes smoked per day, point prevalence abstinence at end of 4 weeks, prolonged and continuous abstinence at 6 months. The measures will be collected throughout the 3-month treatment period as well as at the 6-month follow up. Discussion: This trial will test for the first time the impact of deep insula rTMS on smoking cessation in smokers treated with varenicline. This trial will use an H-coil specific to the insula, while previous studies have targeted both the insula and prefrontal cortex. This trial will inform on the utility to combine insula deep rTMS with varenicline to improve smoking abstinence rates. Clinical Trial Registration: Trial registered at https://clinicaltrials.gov/ct2/show/NCT04083144 (Identifier: NCT04083144).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".